The document is a project report on developing a smart city. It discusses smart cities and highlights existing smart cities like Copenhagen. It then describes a dream smart city and the problems of air pollution faced by cities. The report proposes using soft computing tools like artificial neural networks to predict air quality at locations without monitoring stations based on factors like traffic, meteorology and human mobility. It discusses the methodology, learning process, application and novel aspects of the approach while also acknowledging limitations.

1. Project on Smart Cities
Group 8
Soft ComputingTools in Engineering

2. Group 8
 Rahul Kumar Thakur 13CE10031
 Soumen Saha 13CE10044
 Vipul Bhola 13CE10057
 Vishal Gidwani 13CE10058
 Yogesh Jakhar 13CE10062

3. Contents
 What is a smart city?
 Highlights of Smart City
 Existing state-of-the-art Smart City
 Dream Smart City
 Problem faced & its scope
 Solution to the problem
 Novelity
 Limitations

4.  A Smart City is a holistic ecosystem of different communities,
where people are interconnected and contribute towards a
common vision, creation of knowledge and exchange of ideas.
What is a Smart City?

5. What is a Smart City?
 Smart cities use information and communication technologies (ICT) to be
more intelligent and efficient in the use of resources, resulting in cost and
energy savings, improved service delivery and quality of life, and reduced
environmental footprint—all supporting innovation and the low-carbon
economy.

6. Key aspects of Smart Cities
 a modern digital infrastructure
 a recognition that service delivery is improved by being
citizen centric
 an intelligent physical infrastructure
 an openness to experiment with new approaches
 transparency of outcomes/performance

7. Highlights of Smart Cities in India
 Currently, 31% of India’s
population lives in cities;
these cities also generate
63% of the nation’s economic
activity. These numbers are
rapidly increasing, with
almost half of India’s
population projected to live in
its cities by 2030. Smart
Cities focus on the most
pressing needs and on the
greatest opportunities to
improve quality of life for
residents today and in the
future.

8. Top Smart Cities in theWorld
 Vienna
 Toronto
 Paris
 Berlin
 Copenhagen
 Hong Kong
 Tokyo

9. Smart City Copenhagen
 Copenhagen uses digital technologies to generate a
sustainable economy, to enhance wellbeing and to reduce
costs and resource consumption for its citizens. It uses its
digital infrastructure to find innovative solutions for
transport, energy, healthcare, water, and waste systems.
 Copenhagen is a sustainable and green city, where
inhabitants have a high quality of life, a healthy life and also
have good job opportunities; a people-oriented city using its
resources in a smart way.

10. Smart City Copenhagen
 Copenhagen’s number one initiative is to achieve carbon
dioxide neutrality. It will be the first capital in the world to
become CO2 neutral by 2025. In achieving this goal,
Copenhagen is looking into the energy management of
buildings, traffic issues, and fuel and energy consumption.
 Huge investments have been made into cycling and public
transportation. Specialized lighting and GPS and wireless
systems will fully support the biking transition.These
systems can even calculate the amount of money being
saved.

11. Smart City Copenhagen

12. Dream Smart City

13. Problem faced by Smart Cities
 Air pollution is estimated to be a major environmental health
problem affecting city residents around the world.
 The negative impacts of air pollution include elevated risk of
cardiovascular problems like heart disease and stroke and
more acute asthma. Air pollution may also have negative
consequences for risk of cancer and reproductive or
developmental problems.

14. Problem Statement
 Air Quality Monitoring using Soft ComputingTools

15. Scope
 Many countries are suffering from air pollutions. Many cities
have built a few air quality monitoring stations to inform
people about urban air quality every hour. Influenced by
multiple complex factors, however, urban air quality is highly
skewed in a city, varying by locations significantly and
changing over time differently in different places.
 Thus, we do not know the air quality of a location without a
monitoring station.We do not know what the air quality at a
place will be tomorrow either, let alone the root cause the air
pollution.

16. Background
 Air quality Index
Depends on
 NO2, SO2,O3,CO & PM10
 Why it matters
 Healthcare
 Pollution control and dispersal
 Reality
 Building a measurement station
is not easy
 A limited number of stations
(poor coverage)

17. Air Quality Index Calculation
 An air quality index (AQI) is a number used by government
agencies to communicate to the public how polluted the air
currently is or how polluted it is forecast to become. As the
AQI increases, an increasingly large percentage of the
population is likely to experience increasingly severe adverse
health effects.
 A fuzzy logic approach has been used to calculate the AQI
using membership functions for various pollutants like NO2,
SO2,O3, CO & PM10.

18. Break Point Concentrations And AQIValues

19. Effects of varying air quality

20. Factors affecting Air Quality
Meteorology Traffic POIs Road networksHuman Mobility
Historical air quality data Real-time air quality reports

21. Methodology Overview
 Partition a city into disjoint grids
 Extract features for each grid from its affecting region
 Meteorological features
 Traffic features
 Human mobility features
 POI features
 Road network features
 BP Neural Network learning model forAQI
 Predict the AQI labels using existing AQI data

22. Learning Process
Training
Labeled AQI
Data
Unlabeled AQI
Data
Inference
∆P1x
∆R1x
c
D1Fp
Fr
l1 D2
c
d1x
D1
D2
D1
D1
1
1
Fp
Fr
lk
k
k
Fp
x
Fr
x
lx
∆Pkx
∆Rkx
c
dkx
1
k
x
ANNInput generation
w'11
w'qr
w1
wr
wpq
w11
b1
bq
b'r
b'1
b''

23. Inference Process
∆P1x
∆R1x
c
D1Fp
Fr
l1 D2
c
d1x
D1
D2
D1
D1
1
1
Fp
Fr
lk
k
k
Fp
x
Fr
x
lx
∆Pkx
∆Rkx
c
dkx
1
k
x
ANNInput generation
w'11
w'qr
w1
wr
wpq
w11
b1
bq
b'r
b'1
b''
Inference
AQI at a Location without
Monitoring Station

24. Application
 Urban air quality is of great importance in protecting human health. While there are
limited air-quality-monitor-stations in a city, air quality varies by location
significantly and is influenced by multiple complex factors, such as traffic flow and
land use. Consequently, people cannot know the air quality of a location without a
monitoring station.
 This project infers air-quality information throughout a city, based on air-quality
data reported by existing monitor stations and a variety of data sources observed in
the city, such as meteorology, traffic flow, human mobility, the structure of road
networks, and points of interest.
 This air-quality information could help people figure out when and where to go
jogging—or when they should shut the window or put on a face mask in locations
where air quality is already a daily issue.
 Staff, working with the most vulnerable population, such as children, elderly, and
sick people, can use these data to appropriately assess whether to ventilate the
rooms, lead the children for a walk or onto the playground, and the like.

25. Novelity
 Addressing the challenge of reducing the health effects of poor air
quality in the city cannot be successful without adequate
availability of air quality data.
 Availability of key city air quality information on a single platform
allows city residents to be responsible for their own health.The
platform offers access to the values of air pollution measured by
sensors in real time.This helps people to avoid city areas with
exceeded values, avoid outdoor recreation and limit house
aeration.
 The city administration can analyze these data for many purposes –
even for the assessment whether greening a specific area in the city
with trees will be efficient regarding air quality improvements or
not.And if the city, due to the extremely poor air quality, decides to
close a part of the city for traffic, the well-informed residents may
accept this measure more favorably as otherwise.

26. Novelity
 Cheaper and better that Satellite remote sensing
 Does not reflect ground air quality
 Vary in humidity, temperature, location, and seasons
 More viable as compared to Outsourced sensing devices
 Limited to a few gasses: CO2 and CO
 Sensors for detecting aerosol are not portable: PM10, PM2.5
 A long period of sensing process, 1-2 hours
 Existing methods like linear interpolation do not work
well Price ~ INR 20,00,000

27. Limitations
 Air quality also varies by time
non-linearly
 Affected by many factors
 Weathers, traffic, land use etc.
 Subtle to model with a clear formula
0 40 80 120 160 200 240 280 320 360 400 440 480
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Portition
Deviation of PM2.5 between S12 and S13
>35%
Proportion
A) Beijing (8/24/2012 - 3/8/2013)

28. ThankYou
Group 8:
Rahul Kumar Thakur 13CE10031
Soumen Saha 13CE10044
Vipul Bhola 13CE10057
Vishal Gidwani 13CE10058
Yogesh Jakhar 13CE10062