1. Introduction

2. Proposal : Smart Phone + Image Analysis

4. App Development：Particular Matter Detector

Developing a Particulate Matter (PM)-indicating Smartphone AppMengxuan(Billy) Cai a, Gang (Ian) Chen b, Arthur W.H. Chan b

a Department of Applied Science & Engineering, b Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, Canada

6. Conclusion

CENTRE FOR GLOBAL CHANGE SCIENCE

References and Acknowledgements

Widespread• Around 3 billion people cook

and heat their home using an

open fire or simply burning

biomass and coal1

• PM concentration: 500-1000

µg/m3 during cooking

Health Impacts• Over 4 million people die

prematurely from illness

attributable to the household air

pollution from cooking with

solid fuels1

• Collect the samples (N>3000) by SHARP and scan it by scanner

• Extract RGB values by photo processing

• Establish the correlation between the darkness of the filter paper and

the mass of PM deposited on it

• Plug in the algorithm developed into the app.

References:

1.Kirk R. Smith et al.(2017, July). Millions Dead: How Do We Know and What Does It Mean? Methods Used in

the Comparative Risk Assessment of Household Air Pollution.[Online].Available:

http://www.annualreviews.org/doi/pdf/10.1146/annurev-publhealth-032013-182356

2.World Health Organization.(2017, July). Indoor air pollution and household energy[Online].Available:

http://www.who.int/heli/risks/indoorair/indoorair/en/

Acknowledgements

The authors acknowledge funding through the Center For Global Change Science (CGCS)

3. Method

Air Flow 1 m3/hr

Figure 3. Schematic Diagram of

Synchronized Hybrid Ambient Real-

time Particulate (SHARP) Monitor

Develop an affordable and relatively accurate PM sensor

Figure 1. Health Impact of Solid Fuel

Exposures1

Current Challenges in

PM-sensor Market

Avg. Price = $100 USD = 2.1

months’ household income

Figure 4. CV Filter

Paper Color Picker

Figure 5. MATLAB Machine

Learning Toolbox

Figure 7. The Application Work Flow

Platform: XCode 8

Language: Swift 3.1

Image Analysis Library: OpenCV

Other Open Source Library: Cocoa pod

Image Library

Custom Slider:• Perform edge detection

Figure 6. App Icon

Figure 8. The First View Figure 9. User Guide

Figure 10. The Second View Figure 11. Final View

5. Future Work

PM Exposure Detection for Face Masks’ Sampling

Figure 17. Schematic Diagram of Breathing

Machine

Figure 18. Ambient Particles Concentrator

at Gage Occupational Health

Image Analysis:

• Try other white balance algorithms

• Extract other relevant parameters (e.g., lightness, and saturation)

• iOS

• Less variability on camera

OpenCV:

• White Balance Algorithm: Gray World

Assumption

𝑅average = 𝐺 average = 𝐵 average

• Edge Detection

Custom Slider:• Disk Colour: Using AQI Colour

Estimated PM Exposure: g

0-50 50-100 100-150 150-200 200-300 300-500

Good Moderate Unhealthy

for

Sensitive

Group

Unhealthy Very

Unhealthy

Hazardous

The Second ViewCustom Camera: Paparazzo

• Take photos with the highest

quality

• Back & Front camera

• Flash light

• Rotate the image

• Select canvas & crop image

Figure 12. Photo Capture on Paparazzo.

(a) Camera; (b) Image Cropping

Figure 13.

Origin image

Figure 14.

Grayscale image

Figure 15.

Monochrome image

Figure 16. Air Quality Index (North America)

Filter Paper

• Particulate Matter Samples

Raw Image

• Custom Camera

Image Process

•Auto-White Balance Adjustment

Extract Colour Information

• Edge Detection

Estimate PM Exposure

•Model Build-up

Health Impact Info

•Air Quality Index

• Smartphone image analysis offers a cheap and relatively accurate PM

indicating method

• Future commercialization will not only save millions of peoples’ lives, but

also provide enough evidences for policy making.

• Developing countries

• Free smart phone app

• Easily distributed

Figure 2. Deaths From Indoor Smoke From Solid Fuels2

The Final View

a

b Air Quality Index (North America Standard):