HazeMon

IoT เพือ่การเฝ้าระวงัหมอกควนั

Nisarat Tansakul, Sathita Fakprapai, Sararat Nontachai

and Jutamas Kunphan

at 34th WUNCA

Outline

• HazeMon ?

• Background and Objective

• Hardware and Sensors

• Related Research

– Improvement of Wireless Sensor for Biomass

Burning Smoke Air Pollution Monitoring

– Assessment of exposure to biomass burning

smoke for School children in hotspot areas

of Chiang Rai province, Thailand

– Assessment of Health effects of Biomass

Burning Emission in Chiang Rai Province,

Thailand

HazeMon?

• Low-cost Real-time Monitoring of Haze

Air Quality Disasters in Rural

Communities in Thailand and

Southeast Asia.

• Sponsored by a grant from the French

Ministry of Foreign Affairs and

International Development

• Using Wireless Sensors (IoT)

Background

• In Thailand and Southeast Asia, haze air

quality disasters every year.

• Haze disaster affects the health of people

living in a very large area.

• Government weather stations that report

haze conditions are costly and located

mostly near to large urban cities.

• Rural citizens and farmers still lack timely

capabilities to get the information

regarding haze air quality disaster.

Background

• Low-cost sensors was deployed at

Community Network sites in Tak

Province.

6

Early Warning

Objective

• To build and validate low-cost haze

monitoring sensors that can be

deployed in remote rural areas to

warn rural citizens when too much

haze is present.

Team and Partners• Thailand : Asian Institute of Technology (AIT)

– Internet Education and Research Laboratory (intERLab)

– School of Environment Resources and Development

(SERD)

• France :– Laboratoire Informatique de Paris 6 (LIP6), UPMC, Paris,

France

– Institut national de la santé et de la recherche médicale

(INSERM), Paris, France

• Asia :– Institute of Computer Science, University of the Philippines

Los Baños, Los Baños, The Philippines

– MIIT-University of Kuala Lumpur, KL, Malaysia

Hardware and Sensors

• Hybrid Architecture

– Microcontroller (for sensing)

– Arm-like (for system operations)

• Initial production based UDOO

– WPA-2 Enterprise

– Linux Ubuntu v.s. OpenWRT

Hardware and Sensors

• Temperature

• Relative humidity

• Air pressure

• PM1

• PM2.5

• PM10

• Formaldehyde

Web Interface• Developed with UPMC

– Running on Amazon Docker

– Able to scale

– Easy to use

Site Selection

• 3 difference areas in Chiang Rai

Related Research

• Improvement of Wireless Sensor for

Biomass Burning Smoke Air Pollution

Monitoring

• Assessment of exposure to biomass

burning smoke for School children in

hotspot areas of Chiang Rai province,

Thailand

• Assessment of Health effects of Biomass

Burning Emission in Chiang Rai Province,

Thailand

Related Research

For Biomass Burning Smoke Air Pollution Monitoring

of Wireless Sensors

Improvement and Application

By : Sathita Fakprapai

Haze pollution

88of 3.7 million premature death % Air pollution monitoring

(WHO, 2012)

Objective and Scope

Improve AIT existing wireless

sensors with more sensors & to

test with three different fieldsapplications.

Adding more suitable sensors.

Evaluate the improved sensor nodes.

Field measurement and real time publicizing.

Selection & Assembly

Phase3

Phase 1

Phase 2

Evaluation with referent instruments

Apply to Field & Publish data

NO2, VOCs, PM, CO, CO2

Meteorological parameters &GPS

Particulate matter (PM)

30 times larger than the largest fine particle

(EPA, 2017)

(Thairath, 2016)

(Alen Air Purifiers, 2016)

Volatile organic compounds

(VOCs)

(Libelium, 2017)

Nitrogen dioxide (NO2)

(PCD, 2016)

(NASA, 2017)

Carbon monoxide (CO)

Carbon dioxide (CO2)

(Yokohamathailand, 2017)

Microcontroller Arduino YUN

Transceiver 3G/Wifi

GPS module Grove GPS

Software Arduino Software (IDE)

NO2 sensor 1,390

VOCs sensor 840

Dust sensor 790

CO sensor 120

CO2 sensor 1,250

Temp & Humidity 690

GPS module 1,250

Acrylic Box 200

3G Air card 1,000

SD card 786

Arduino YUN 2,800

Base shield 390

Total 11,506

Assembly sensor box

Literature Review

Sensor Selection

Suitable Sensors

Assembly Sensor Nodes

Evaluation

Apply to Field Monitoring and Publicizing Data

Discussion and Conclusion

Methodology

Sensors evaluation

AIT/Rice Field Burning PCD monitoring stations Chaingrai

AIT/Rice Field Burning

PCD monitoring stations

Sensor nodes will consist of suitable VOCs, NO2,

CO, CO2, PM2.5, PM10 and BC sensors.

The calibration results will be acceptable value.

The nodes will be apply at field and report real-time data.

Expected results

Demonstrate sensor box

https://thingspeak.com/channels/209860/private_show

Link for data monitoring

ASSESSMENT OF EXPOSURE TO BIOMASS

BURNING SMOKE FOR SCHOOL CHILDREN

IN HOTSPOT AREAS OF CHIANG RAI

PROVINCE, THAILAND

Master student of Environmental Engineering and Management

School of Environment, Resources and Development

By : Sararat Nontachai

Asian Institute of Technology

(AIT)

OBJECTIVES

The overall goal is to assess the exposure levels and

potential health effects of emission from biomass burning.

The concrete objectives are as follows:

To assess ambient air pollution in selected study areas

during burning and non-burning periods

To conduct exposure assessment

by using personal exposure monitoring

To comparatively analyze air pollution and exposure levels

during burning and non-burning periods

6/23

SCOPE OF STUDY

Focusing on exposure of school children

to biomass burning in Chiang Rai province

The monitoring is conducted in two period;

non-burning and burning period

Ambient monitoring and personal monitoring;

PM2.5 and VOCs are collected

Survey, questionnaires, time-activity diaries,

direct observation, laboratory experiment

as well as exposure model will be applied

to assess exposure to pollutants from biomass burning.

7/23

FRAMEWORK OF METRODOLOGY

9/23

Chiang Rai: selected area

Burning period Non-burning period

Ambient monitoring:

PM2.5 and BTEXQuestionnaires and spirometric test

of 100 students

Personal monitoring PM2.5, BTEX :

(2 subjects/season)

Personal exposure concentration

Data compilation and analysis

Conclusions& recommendations

Ambient monitoring:

Mirror foundation

Ban Huai Khom School (BHKS)

Personal exposure monitoring:

Student from BHKS

Student from Ban Thung Luang

Exposure model

Health

SITE DESCRIPTION

Chiang Rai

Source: Google Maps (2016)

▣ Mueang district was selected as the one of intensive burning area in

Chiang Rai. ▣ High frequency of hotspots, easy to access, convenience (telephone,

electricity) and safety are the reason for selecting.

10/23

SITE SELECTION

• Mae Yao sub-district has large amount of hotspots arising• This sub-district is easy to access

• The ambient monitoring stations HAZEMON projects will be located there

• PCD monitoring stations is nearby

In this study, Mae Yao sub-

district is selected as a

monitoring area

Source: Google Maps (2016)

12/23

SITE OF MONITORING STATION

Distance from Mirror

foundation to

Ban Huai Khom School

around 1.5 km

13/23

Mirror foundation and

Ban Huai Khom School (BHKS)

are selected for ambient air monitoring

Mirror foundation

Ban Huai Khom School

The selected participants will need to meet certain criteria, including

1)participant’s age vary from 10-12 year-old.

2) the household is not close to the roads.

3)Non-smoking residents.

4) the main cooking fuel is LPG.

SELECTION OF SCHOOLS AND

SCHOOL CHILDREN

• There are many school childrean in Mae Yao sub-district.

• Ban Huai Khom School (BHKS) and

Ban Thungluang school are selected for conducting questionnaires.

• School children are selected as a target group of this study.

• Subjects are selected from surveys and questionnaires.

14/23

SITE OF SELECTED SCHOOL

15/23

Ban Huai Khom School

Ban Huai Khom school

Monitor

PM2.5 : MiniVol and GRIMM

BTEX : Charcoal coconut tube

Ban Thungluang school

Ban Thung luang School Distance from BTLS to

BHKS around 2.8 km

SAMPLING DESIGN AND

SAMPLE SIZE

• The sample size will be calculated by Taro Yamane equation

(Equation 1).

Where:

n: Sample size

N: Total number of population

e: The acceptable error (10%)

▣ This study focuses on children population who are 10-12 years old.

▣ The total number of population in selected group is around 1104.

▣ The sample size should be 100

▣ 2 subjects who meet criteria and not meet criteria selected from surveys and questionnaires

to conduct PEM and SKC charcoal coconut tube.

𝑛 =𝑁

(1 + 𝑁𝑒2)

16/23

SUMMARY OF SAMPLING PLAN

Ambient monitoring

(2 locations)

Personal monitoring

( 2 students)

Equipment

- MiniVol: PM2.5

- CO : online data

- SKC charcoal coconut tube with personal

pump for BTEX

- PEM: PM2.5

- SKC charcoal coconut tube with personal

pump for BTEX

Sampling media

- Quartz filter ∅47mm: PM2.5 mass (Ambient monitoring)

- Quartz filter ∅37mm: PM2.5 mass (Personal monitoring)

- Online measurement: CO

- SKC-charcoal coconut tube: BTEX

Sampling duration - 24 hour sampling - 24 hour sampling

Sampling period

- Non burning period: December 6 – February 5, 2016

- Burning period: February 15 – March 20, 2017

17/23

MONITORING SITE

18/23

Ambient monitoring

Personal monitoring

DATA COLLECTION

Surveys Questionnaires Time activity diaries Direct observation

OTHER DATA COLLECTION

Spirometric test

Air pollution dataMeteorology data

Hospital records

19/23

DATA ANALYSIS

Exposure modeling

▣The total exposure of subjects to each

pollutant was calculated by exposure model

▣ Input data: Exposed concentrations and time

of subjects spend in each environment

(obtained from time activities diaries)

▣ Output data: Exposure concentration of each

pollutant during a period 24 hours.

Statistical data analysis

▣ To test the difference in personal

exposure among participants, a paired

t-test will be applied.

▣ To study the relationship between

biomass burning with haze air pollution,

a Pearson correlation coefficient

analysis will be applied.

20/23

QUALITY ASSURANCE AND

QUALITY CONTROL (QA/QC)

Sampling and Analysis

Parameters Measurement method Calibration method

PM2.5 Microbalance USEPA (1998)

BTEX GC/FID NIOSH 1501 (NIOSH, 2003)

21/23

EXPECTED OUTCOMES

A comparison of air pollution concentration in

burning season and non-burning period in Mae

Yao sub-district, Mueang district, Chiang Rai

Exposure concentration of PM2.5 and BTEX

from selected group

The potential health effects between burning and

non-burning period

23/23

ASSESSMENT OF HEALTH EFFECTS OF

BIOMASS BURNING EMISSION IN CHIANG RAI PROVINCE, THAILAND

Environmental Engineering and Management

School of Environment, Resources and Development

Asian Institute of Technology, Thailand

Ms. Jutamas KunphanMaster’s student

To assess health effects of biomass burning

emission in Chiang Rai province, Thailand.

Overall objective

1. To analyze hospital records in relation to air pollution levels

(PM10, CO).

2. To assess air pollution levels during burning and non-burning

seasons in relation to the hotspot counts and meteorological

data.

3. To assess the deposition potential of haze particles in the

human respiratory system.

Specific objectives

OBJECTIVES

SCOPE OF STUDY

The study focus on analysis of hospital records during six

years (2011-2016) in Chiang Rai province, Thailand.

Air pollution level focusing on PM10, CO, and fire hotspots are

analyzed with the hospital records to reveal relationships.

The MPPD model will be applied to assess the

deposition rate of haze particles in human lungs.

The particle size distributions during haze and non-haze periods

required for MPPD modeling determined by a cascade impactor.

METHODOLOGY FRAMEWORK

Phase 2: Data analysis

Conclusions and recommendations

Phase 1 : Data collection

Data analysis

Relationship between air pollution

level and Hospital records

Relationship between hospital records, air

pollution level, meteorological and hotspot data

Input data of MPPD model(Measurement PM by using a cascade impactor

MPPD model

Output data

PCA

Ambient air monitoring data

(CO and PM10)

Hospital records

(Involve with air pollution)

Hotspot data (from MODIS

satellite)

GAM

Meteorologicaldata

Select the study area

(Chiang Rai province, Thailand)

Select the study period (2011-2016)

METHODOLOGY(Data collection)

Ambient air monitoring data (CO and PM10) from 2 PCD

monitoring stations in Chiang Rai

Hospital records from Hospital in Chiang Rai

Satellite data (Hotspot) from Fire Information for Resource

Management System (FIRMS) website

Input data of MPPD model (Particle concentration will be

sampling by using a cascade impactor)

The collected daily data of hospital

records and daily concentrations of

air pollutants (PM10 and CO)

Poisson regression with generalized additive model (GAM)

To reveal relationships between the

pollution levels and daily numbers of

patients in different conditions (haze and non-haze periods)

Using SPSS software to analysis

Generalized Additive Model (GAM)

METHODOLOGY(Data analysis)

ParametersCO (ppm)

PM10 (µg/m3)

Wind speed (m/s)

Surface temperature (°C)

Relative humidity (%)

Air pressure (mb)

hospital records

Biomass burning hotspots

All of these will be analyzed by

the PCA from SPSS software to

reveals the relationships.

Principal Component Analysis (PCA)

Stage Size range (µm)

0 9.0 – 10.0

1 5.8 – 9.0

2 4.7 – 5.8

3 3.3 – 4.7

4 2.1 – 3.3

5 1.1 – 2.1

6 0.7 – 1.1

7 0.4 – 0.7

backup filter 0-0.4

Particle Size Data

Input data of MPPD model

(Particle concentration)

Cascade impactor

● Particle concentration● Breathing frequency● Tidal volume● Inhalation fraction● Pause fraction

Input data of MPPD model

Data collection both burning

and non-burning seasons.

Multiple-Path Particle Dosimetry Model

(MPPD v 2.11)

Input data of MPPD model

(Particle concentration)

MONITORING SITE

At the Mirror Foundation,Chiang Rai

EXPECTED RESULTS

The number of patients has positive correlation with the PM10

and CO concentration level in burning season.

Health incidence rate during burning and non-burning

season are significant difference.

The correlation demonstrate between air pollution

concentration (PM10, CO) and number of hotspot.

Prediction deposition patterns in the human respiratory tract

of particle size distribution between burning and non-burning

season are available.

Conclusion

• HazeMon is an example of research

which is extended to real community.

• We should starting to plan how we can

extend our research to community.