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Optimal Air Quality Monitoring Network for Green Cities

International Journal of Mohammed Mujtaba Shareef1*, Tahir Husain1, and Badr Alharbi2
Environment and 1Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL,
Sustainability [IJES] A1B 3X5, Canada
ISSN 1927-9566 2National Center for Environmental Technology, King Abdulaziz City for Science
Vol. 5 No. 2, pp. 72-88 and Technology, Riyadh 11442, Saudi Arabia
(2016)
Abstract. Continuous efforts are being made by cities around the world to iden-
*Correspondence: tify methods to make their environment healthier and more livable through the
mms515@mun.ca improvement of water, land and air. Air pollution is of high importance since it
often causes severe and irreversible damage to human health and environment
and remediation is extremely difficult. The potential sources of air pollution, such
as large industries, automobiles and power plants, are essential part of cities.
Transforming these cities into “green cities” necessitates control of these emis-
sions. Continuous monitoring of air pollution with a well-designed air quality
monitoring network (AQMN) is usually the first step in addressing and tackling
the emissions. Environmental protection agencies are looking for an optimal de-
signed AQMN with an obvious focus on minimizing costs, along with other objec-
tives. This article proposes a simple method of optimizing the AQMN using geo-
graphical information system (GIS), interpolation techniques and historical data.
Existing air quality stations are systematically eliminated, and the missing data is
filled in with data generated from the most appropriate interpolation technique.
The interpolated data is then compared with the observed data. Pre-defined per-
formance measures were used to check the accuracy of the interpolated data. An
algorithm was developed in a GIS environment and the process was simulated for
several sets of measurements. This methodology proved to be useful to decision
makers to find optimal numbers of stations that are needed without compromis-
ing the coverage of the concentrations across the city.

Keywords. air quality monitoring, GIS, green cities, interpolation, Kriging, IDW

1. Introduction The methodology used to design a new AQMN or
evaluate an existing AQMN attracted the atten-
It is well known that air pollution causes adverse tion of several researchers. Maximum sensitivity
effects on human health and negatively impact of the collected data (Koda and Seinfeld, 1978;
the environment. Due to rapid urbanization and Caselton and Husain, 1980) and maximum cov-
industrialization, air pollution is of high signifi- erage factors such as intensity of emissions,
cance, particularly in large cities, when it comes source distance and meteorology (Houghland et
to keeping the city green. Continuous monitoring al., 1980) were some of the first techniques used
of air pollution with a well-designed air quality to design an AQMN. Statistical measurements of
monitoring network (AQMN) is the first step in information content (Picket and Whiting, 1981)
addressing this issue. Obtaining continuously and Fisher’s information measure (Husain and
monitored data to ensure safe levels of air qual- Khan, 1983) was used to determine the optimum
ity is one of the primary objectives of AQMN in number and location of monitors in a network.
addition to evaluating exposure hazards and im- The design of an AQMN in the greater London
plementing effective control strategies. Environ- area was conducted by Handscombe and Elson
mental protection agencies are looking for an op-
timal design of AQMN meeting these objectives
with an obvious focus on minimizing cost.