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ODORSENSE: MEASURING, ASSESSMENT AND
ALERTING THE HEALTH EFFECTS OF ODOR
POLLUTION
Santhosh B. Panjagal
Research Scholar, VTU Belagavi. Associate Processor, KEC-Kuppam, (India).
E-mail: santupanjagal@gmail.com
ORCID: https://orcid.org/0000-0002-6263-1727
G. N. Kodanda Ramaiah
Professor & Dean, KEC-Kuppam, (India).
E-mail: gnk.ramaiah@gmail.com
ORCID: https://orcid.org/0000-0002-1692-9629
Recepción:
09/12/2020
Aceptación:
02/03/2021
Publicación:
07/05/2021
Citación sugerida:
Panjagal, S. B., y Ramaiah, G. N. K. (2021). OdorSense: Measuring, Assessment and Alerting the
Health Eects of Odor Pollution. 3C Tecnología. Glosas de innovación aplicadas a la pyme, Edición Especial,
(mayo 2021), 97-113. https://doi.org/10.17993/3ctecno.2021.specialissue7.97-113
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ABSTRACT
Nowadays there is an increased conict between residents and government bodies /or
industries due to unpleasant or oensive Odor smells emanating from dierent sources,
interfacing with person’s enjoyment of life as they are frequent and persistent. The main
concern among all the residents is the health eects of toxic Odor gases (like, ammonia,
Sulphur dioxide, nitrogen, hydrogen sulphide) released from the waste dumping sites,
drainages, food & meat processing industries, etc., causing dreadful diseases to the living
beings. There is urgent need of an intelligent mechanism, which allows every common
people access the Odor pollution information through user friendly applications. Hence
the main objective of the proposed research work was to develop an intelligent mechanism
for detecting, measuring and alerting the health eects of Odor pollution. The research
work follows design o an articial olfaction system based electronic nose using low cost, low
power and improved accuracy sensors for detection and real-time measurement of Odor
concentrations at various sources of Odor emissions, uploading the Odor concentrations to
IoT cloud for remote monitoring and alerting. User friendly interface application developed
for providing real time information about the Odor levels at the desired source and alerting
the health eects if the Odor concentration levels increases above the threshold levels.
KEYWORDS
Odor pollution, Odor Measurement, Odor concentration, Articial olfaction system
(E-Nose), User Interface (Mobile Application), Health survey, IoT Cloud, Risk Assessment.
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1. INTRODUCTION
Odor pollution is most complex problem due to its distinctly dierent characteristics, as they
possess dierent physical and chemical properties, and they are present at a concentration
ranges from high parts per million (ppm) to low parts per billion (ppb) (Shinde et al., 2017).
Till date very little attention has been paid towards Odor pollution issues in India, therefore
Odor pollution and its problems has become objectionable proportion with the growing
population, industrialization, and urbanization. The main cause of Odor pollution and
its problems is due to urbanization with improper sanitation facilities (Nicolas et al., 2006).
At the same time there are many sources which contributes to Odor pollution, they are
classied as 1. Point sources (vents, stacks, and exhausts), 2. Area sources (Sewage treatment
plants, wastewater treatment plants, solid waste landll, composting, household manure
settling lagoons, etc.) (Elwell, 2001; Nicolas, 2006; Pagans, 2006), 3. Building sources (Pig
sheds, hog connement chickens) (Misselbrook et al., 1993) 4. Fugitive sources (soil bed or
bio-lter surfaces). Hence, Odor can arise from many sources, most are manmade garbage
or unscientic dumping on vacant lands (Di et al., 2013; CPCB, 2007).
The Major Odor pollution are Industries Pulp & Paper, Fertilizer, Pesticides, Tanneries,
Sugar & Distillery, Chemical, Dye & Dye Intermediates, Bulk Drugs & Pharmaceuticals etc.,
Large Livestock operations, Poultry/chicken Farms (Hayes et al., 2006), Slaughterhouses,
Food processing industries, Agricultural activities like decaying of vegetation, production
and application of compost etc. (Yan-li Zhu, 2016; CPCB, 2007), In urban and metropolitan
areas, improper maintenance of public amenities like toilets, bus/railway stations, hospitals,
shopping complex etc. generate pungent Odor, which aects the peoples as well as
neighborhood residents. Important issue is Odor cannot escape from Congested markets,
thus causing problems to shop-keepers as well as to customers (CPCB, 2007).
Generally, the most common Odors released from various sources are putrid, pungent, or
musky etc., from these Odors some toxic gases are also released like ammonia, Sulphur
dioxide, hydrogen sulde (Sarkar et al., 2002) which can cause dreadful diseases to living
beings, hence strong Odors released from dierent sources, causes irritation to eyes, nose,
throat or lungs, nausea, loss of memory & sleep, coughing due burning sensation, headache,
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dizziness, wheezing or other breathing/respiratory problems (NIOSH, 2007, OSHA, 2011,
ACGIH, 2021).
Now the Odor pollution nuisance has become most important environmental issue among
all pollution problems, leading to more number complaints and conicts between residents,
industries and government (Nicolas et al., 2006; Takano, 2014). Therefore, demanding
more stringent policies to regulate Odor annoyance and need for continuous eorts to
manage and limit the Odor exposure in the neighborhood to identifying, quantifying and
monitoring the Odor emission (Wenjing et al., 2015).
Generally, there are various measurement techniques to quantify the Odor concentrations,
among them some techniques (human olfaction and conventional analytical) provide only
perception real Odor and mixture composition, but not applicable for continuous real-time
measurement in the eld and also don not provide global information relating to Odor
perception (Nicolas et al., 2006). There is a need to develop an appropriate new system
to measure and monitor Odor concentration based on devices rather than depending on
biological human olfaction involving trained human panelists (Deshmukh et al., 2017).
In this research study, an embedded electronic device is designed to measure the
concentration of Odor pollution at selected areas. The design of articial olfactory
electronic nose (Gongora et al., 2019; Erisman, 2001) incorporates most desirable industrial
Odor sensors; TGS2602 Metal Oxide Semiconductor (MOS) type Sensor for detecting
Hydrogen Sulde (H2S), Ammonia (NH3), Volatile Organic Compounds (VOCs) and
110-601 Sulphur Dioxide (SO2) sensor, Solar harvesting unit for powering the portable
electronic nose, Odor threshold level indicator, Communication network unit (Wi-Fi) for
uploading processed data onto the IoT cloud storage and mobile application for retrieving
the Odor concentration from IoT cloud, performing risk assessment based on standard
threshold levels (EPA, 2021) and then providing the alert information to end users. Finally,
the Data processor (Atmega328) coordinates and controls all the peripherals connected to
the system, processes the sensor data and sends the sensed information to the Cloud.
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2. MATERIALS AND METHODS
2.1. STUDY AREA
The dierent odour emission sources in and around the Kuppam Town, Andhra Pradesh,
India, selected for studying the Odour pollution and its Risk assessment based on standard
odour threshold levels. The most commonly selected odour emission sources (areas) are;
agriculture practices (fertilizer, pesticides), large livestock operations, poultry farms, sh
market, drainages, Municipal Solid waste dumping yards & public amenities like toilets of
cinema hall, bus / railway stations, hospitals, shopping complex, etc.
Some of the Municipal Solid waste dumping yards, drainages, sh markets in Kuppam
areas as shown in the Figure 1.
Figure 1. Study areas like sh market, landlls and drainages etc. Images taken at Kuppam.
Source: own elaboration.
The selected study area in Kuppam, weather in the wet season is muggy and overcast, the
dry season is partly cloudy, and it is hot year round. The temperature typically varies from
18°C to 38°C, Average rainfall varies from 4 mm to 124 mm, Extreme variation in the
perceived humidity varies from 29% to 89% and the seasonal variation of the wind speed
over the course of the year is 2.7 m/s to 6.1 m/s (Weather Spark, 2020).
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2.2. METHODOLOGY
The development of methods includes:
• Design of articial olfactory Electronic device (E-Nose).
• Mathematical modelling.
• Measurement of Odour concentration & Sampling.
• Data processing, Storage & Remote Monitoring.
• Risk assessment & Standard Guidelines.
The odour concentration were measured using articial olfactory Electronic device called
Electronic Nose from dierent sources of odour emission, risk assessment was done based
on the standard safety and threshold concentration levels prescribed by like Occupational
Safety and Health Administration (OSHA), National Institute for Occupational Safety and
Health (NIOSH) (NIOSH, 2007; OSHA, 2011).
2.2.1. DESIGN OF ARTIFICIAL OLFACTORY ELECTRONIC DEVICE (E-NOSE)
In this research study, an embedded electronic device is designed to measure the
concentration of odour pollution at selected areas. The design of articial olfactory
electronic nose incorporates most desirable industrial Odour sensors; TGS2602 Metal Oxide
Semiconductor (MOS) type Sensor for detecting Hydrogen Sulphide (H2S), Ammonia
(NH3), Volatile Organic Compounds (VOCs) and 110-601 Sulphur Dioxide (SO2) sensor,
Solar harvesting unit for powering the portable electronic nose, Odour threshold level
indicator, Communication network unit (Wi-Fi) for uploading processed data onto the IoT
cloud storage and mobile application for retrieving the odour concentration from IoT cloud
, performing risk assessment based on standard threshold levels and then providing the alert
information to end users. Finally, the Data processor (Atmega328) coordinates and controls
all the peripherals connected to the system, processes the sensor data and sends the sensed
information to the Cloud. The articial olfactory Electronic device (E-Nose) design block
diagram is as shown in Figure 2.
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Figure 2. Block diagram of Proposed Electronic Node Design.
Source: own elaboration.
2.2.2. MATHEMATICAL MODELLING
The mathematical modelling involves the development the mathematical algorithms
for calculating sensor coecients (calculating sensor resistance (Ro) in fresh air, sensor
resistance (Rs) in displayed gases at various concentrations) from the sensitivity curve of
sensor datasheet and converting the analog output values of the odour sensors into Parts
Per Millions (PPM) standard.
Sensor coecients can be calculated in to ways either using straight line equation or using
power Regression analysis.
To calculate the sensor coecients using straight line equations sensitivity curve and the
basic measuring circuit of the sensor is considered as shown in Figure 3.
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Figure 3. Basic measuring circuit of TGS2602 sensor.
Source: TGS2602 Sensor Datasheet, FIGARO USA, INC.
The sensor resistance Rs can be determined using Ohm’s Law: V = I x R
from basic measuring circuit is output current is equal to: I = VC / (Rs+RL)
Ro/RL: sensor resistance in the clean air.
Rs: sensor resistance at various concentrations of gases.
Then; Rs = [(Vc x RL) / VRL] – RL (1)
Equation 1 will help us nd the values of the sensor resistance for dierent gases. To calculate
sensor resistance Ro, the value of the RS in fresh air needs to be determined. This is done
by taking the average of analog readings from the sensor and converting it to voltage.
The sensitivity curve/graph of the sensor is in log-log scale, so the straight-line equation for
nding coecients is;
y = mx + b
Where: y: Y value on Y axis
VOUT (VRL)
GAS
(+)
(-)
1
4
R
H
RS
3
(+)
(-)
2
R
L
V
H
V
C
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x: X value on X axis
m: Slope of the line
b: Y intercept
For a log-log scale, the formula looks like; log(y) = m*log(x) + b
Now to nd the slope(m), 2 points needs to be chosen from the sensitivity curve/graph.
The formula to calculate m is the following:
y = mx + b (2)
where: value of x: X value on x-axis
value of m: Slope of line
b: Y interception Point on Graph (x1,y1) (x2,y2)
m = [log(y) - log(y0)] / [log(x) - log(x0)]
b = log(y) - m*log(x),
log(y) = m*log(x) + b,
This is how the two coecients (m, b) are calculated using straight lines equations of
sensitivity curve.
Another method to calculate coecients (m, b) is using power regression based digitizing
software tool, which converts the image le showing the sensor graph/curve into digital
numbers. The digitizer tool recovers the data points from sensor graphs. The calculated
data points are usually used as input to other software applications or programming the
controllers to measure gas concentrations accurately from the sensors.
After nding the Coecients, slope m and b using straight line equation method or power
regression method from sensor gas sensitivity curve, nally calculating the gas concentration
in ppm using the equation 3;
X in ppm = 10^ ((log (Rs/Ro) – b) / m) (3)
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2.2.3. MEASUREMENT OF ODOR CONCENTRATION & SAMPLING METHOD
From decades mainly research studies on odor concentration measurement were based
laboratory testing, which means collection of Odor samples from dierent test elds and
then measuring concentration of dierent odor gases at the laboratory.
In our research study we developed a customized electronic device (E-Nose) using Dierent
odor sensors to determine the concentration of various odor gases at selected areas.
Before the development of odor sensing device, rstly, we selected study areas and visited
to investigate problems persisted in and around the study areas due to odor pollution.
Conducted real-time survey by interacted with the residents staying around the selected
study areas with various questionnaires related to health issues, lifestyle, frequency of
occurrence, duration, etc., Secondly, we developed a customized electronic device (E-Nose)
for measuring odor concentration at the eld under test. Thirdly, we carried the Autonomous
E-Nose odor sensing device for measuring odor concentration at various selected areas.
Repeated the measurements were done for many days and then uploaded the measured
odor concentrations to IoT cloud for further studies and performing risk assessment based
on the measured levels.
2.2.4. DATA PROCESSING, STORAGE & REMOTE MONITORING
The data collection starts with the basic survey where the research study begins and continued
till the real-time eld measurement of odor concertation will be done, hence the data needs
to be processed at various stages of the research development. The data processor reads
odor levels from the odor sensors, then processes it by applying mathematical modelling
algorithms to convert the output voltage levels into odor concentration in parts per million
(ppm). After data processing, measured odor concentration data was uploaded to IoT cloud
for storage and further studies. The data stored onto the IoT cloud helps us to further
assess the data using user-friendly user interfaces like mobile/web applications or remote
monitoring and decision-making purposes.
2.2.5. RISK ASSESSMENT & STANDARD GUIDELINES
As we know that constant exposure to odor pollution causes many health issues, therefore
risk assessment must be done comparing the measured odor concentration levels at various
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areas against the standard Threshold Limit Levels (TLV) of various Odor gases published
by agencies like Occupational Safety and Health Administration (OSHA), National Institute
for Occupational Safety and Health (NIOSH), American Conference of Governmental
Industrial Hygienists (ACGIH) (NIOSH, 2007; OSHA, 2011; ACGIH, 2021). Risk
assessment was performed based on the measured odor concentration and permissible
Threshold Limit Levels (TLV). After assessment, if the concentration of measured odor
gas exceeds threshold limit values, then an alert indication with symptoms will be displayed
on user application.
Table 1 shows the Standard Threshold Limit Levels (TLV), 8-hour Time-weighted Average
(TWA), Short Term Exposure Limit Values (STEL) and Immediately Dangerous to Life &
Health (IDLH),
Table 1. Threshold Limit values of various odor gases concentration in parts per million (ppm).
Odor Gas TLV-TWA TLV-STEL IDLH
Ammonia (NH3) 25 35 300
Hydrogen Sulde (H2S) 10 15 100
Sulphur Dioxide, SO2 2 5 100
Sources: TLV-TWA and TLV-STEL data extracted from the 2005 Threshold Limit Values & Biological Exposure
Indices, copyright 2005 by the American Conference of Governmental Industrial Hygienists (ACGIH). IDLH
values extracted from the NIOSH Pocket Guide to Chemical Hazards, 2004 published by the National Institute
for Occupational Safety and Health (NIOSH).
3. RESULTS AND DISCUSSIONS
In this current research work, we followed new approach of measuring odor concentrations
based on electronic devices rather than on human sensory olfaction methods. Figure 4 shows
a self-powered portable electronic device (E-Nose) designed to measure the concertation of
odour gases at selected areas.
In rst phase we conducted real-time health survey of residents and peoples staying near
the selected study areas like; waste dumping yard, Drainages, agriculture practices, livestock
operations, industries and poultry forms. The survey involved the questionnaires in the
form age, gender, type of health issue (disease), frequency of occurrence & duration. In
this sampling survey around 80 peoples were interacted and collected the information
mentioned in the questionnaire. The real-time survey data is shown in Table 2.
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Figure 4. Proposed Odor Measuring Portable Electronic Hardware Device.
Source: own elaboration.
Table 2 gives information about real-time survey at dierent areas, various symptoms or
health issues reported by interacting with peoples staying at the areas and proximity areas.
Most of the peoples reported the frequency of occurrence & duration of these symptoms
is around 1 week to 1 month.
Table 2. Conducted Real-time Health survey data at different areas (Kuppam area).
Study Area Health Symptoms No. of Health issues reported
Waste Dumping Yard
Nausea, Coughing 35
Headache, Loss of sleep 40
Irritation (eyes, throat, nose) Respiratory
problems
39
Fatigue & Dizziness 37
Drainage at Residents
Nausea, Coughing 43
Headache, Loss of sleep 40
Irritation (eyes, throat, nose) Respiratory
problems
38
Fatigue & Dizziness 32
Agriculture practices
Pesticides
fertilizers
Nausea, Coughing 53
Headache, Loss of sleep 49
Irritation (eyes, throat, nose) Respiratory
problems
29
Fatigue & Dizziness 42
Livestock Operations
Nausea, Coughing
Headache, Loss of
sleep
Nausea, Coughing 33
Headache, Loss of sleep 30
Irritation (eyes, throat, nose) Respiratory
problems
28
Fatigue & Dizziness 22
Source: own elaboration.
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In the second phase, we conducted real-time measurement of odour concentration at
selected areas. The table III shows the measured odour concentrations of Ammonia (NH3),
Hydrogen Sulphide (H2S) & Sulphur Dioxide (SO2) by considering temperature, humidity
and wind speed & direction, measurement is conducted morning to evening at the every
1hr duration.
Table 3. Conducted Real-Time measurement of odour concentration at different sites.
Study Area Ranges of Measured Odour Concentration in ppm
Waste Dumping Yard NH3 H2S SO2
Drainage at Residents 15-105 0.5 - 12 0.25 – 1.5
Agriculture practices 10-85 1.5 - 11 0.5 – 2.0
Fertilizers, pesticides 45-115 2 - 15 1.5 – 4.5
Livestock Operations 5-95 0.85 - 8.5 0.2 – 1.25
Source: own elaboration.
Table 3 shows the measured odour concentration at dierent sites, at waste dumping site
NH3 levels ranges from 15-105 ppm, H2S levels ranges from 0.5 - 12ppm, SO2 levels
ranges from 0.25 – 1.5ppm. Drainage at Residents NH3 levels ranges from 10-85 ppm,
H2S levels ranges from 1.5 - 11ppm, SO2 levels ranges from 0.5 – 2.0 ppm. Agriculture
practices Fertilizers, pesticides NH3 levels ranges from 45-115 ppm, H2S levels ranges from
2 – 15 ppm, SO2 levels ranges from 1.5 – 4.5 ppm. Livestock Operations NH3 levels ranges
from 5-95 ppm, H2S levels ranges from 0.85 - 8.5 ppm, SO2 levels ranges from 0.2 – 1.25
ppm. The rage of odour concentrations at dierent sites are clearly showing exceed in its
levels i.e 8-hr Time Weighted Average (TWA) Threshold Limit Value (TLV) and Short
Time Exposure Limit (STEL) Threshold Limit Value (TLV) as shown in Table 2. According
to OSHA, NIOSH and ACGIC regulation the symptoms or health issues associated with
the measured concentration ranges are (ACGIH, 2021; NIOSH, 2011) nausea, coughing,
headache, loss of sleep, irritation (eyes, throat, nose), respiratory problems, fatigue &
dizziness, etc.
Finally measured odour concertation levels are uploaded to IoT cloud and further studies.
The data stored onto the IoT cloud helps us to further assess the health impacts associated
with odour levels using user-friendly mobile/web applications. After risk assessment, if
odour levels are exceeds the threshold limit values, then precautionary alert is generated as
shown in Figure 5. IoT application displays Odour concentration levels in ppm at measures
site on the map as well as in the dashboard.
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Figure 5. Design of IoT Mobile Application.
Source: Developed on MIT APP Inventor 2.
4. CONCLUSIONS
We successfully designed an OdorSense a solar-powered, portable handheld electronic
device, and conducted the real-time measurement of odor gas concentrations at various
study areas. The device has shown satisfactory results in measuring the odor levels. A real-
time health survey has been conducted at selected study areas involving around 80 peoples
in the sampling process based on questionnaires. After conducting the odor concentration
measurement, a Risk assessment was performed to check the associated health issues based
on the measured odor concentration and permissible Threshold Limit Values (TLV) i.e
TLV-TWA and TLV-STEL. After the assessment, if the concentration of measured odor
gas exceeds threshold limit values, then an alert indication with symptoms will be displayed
on the user application. Hence a user-friendly system was developed to measure and assess
the odor pollution at any application.
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5. ACKNOWLEDGEMENTS
The author of research paper would like to express gratitude to G. N. Kodanda Ramaiah,
Professor & Director R&D, Kuppam Engineering College for supporting and guiding
throughout the research work. Also grateful to the Dr. Rangaraju and Dr. Vijay Prakash,
Doctoral Committee members or giving valuable suggestions or carrying out research
work. Finally, I feel thankful to KEC management for the motivation and research facilities
provided in the R&D Centre in the Institution.
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