


































Energy and Earth Science 
Vol. 4, No. 1, 2021 

www.scholink.org/ojs/index.php/ees 

ISSN 2578-1359 (Print)   ISSN 2578-1367 (Online) 

1 
 

Original Paper 

Measurement of Surface Area Concentration of Fine Particulate 

Matter in Indoor Environments 

Pramod Pai
1 

1 
Department of Physics, PES Institute of Technology and Management, Shivamogga, India 

 

Received: November 2, 2020   Accepted: November 30, 2020   Online Published: January 31, 2021 

doi:10.22158/ees.v4n1p1          URL: http://dx.doi.org/10.22158/ees.v4n1p1 

 

Abstract 

Indoor exposure to fine particles (0.01-2.5 µm) is measured in the present study using Surface Area 

Concentration (SAC) metric for two indoor environment categories based on the cooking fuel 

used—kerosene and Liquid Petroleum Gas LPG. Study of SAC is very important from the point of view 

of health aspects. From the collected data, Arithmetic Means, Geometric Means, peak concentration 

values were determined to compare between the two categories of Indoor environment. The results 

show the two environments to be significantly different. Also, the results indicate remarkable high 

indoor surface area concentrations during the cooking and other combustion processes. 

Keywords 

Fine particulate matter, surface area concentration, air quality, indoor combustion, real-time 

measurement 

 

1. Introduction 

Solid or liquid particles suspended in air form aerosols. Particulate portion is usually referred to as 

Particulate Matter (PM). PM is chemically and physically diverse substance and range in a wide size 

range. Aerosols are generated from various sources such as dust, soil, volcanic eruptions or due to 

anthropogenic processes like combustion activities, agricultural, commercial or industrial practices. 

Then they also change because of processes such as condensation, nucleation etc. For indoor 

concentration of aerosols, we can consider the following as the sources: cooking, smoking and other 

combustion activities, pollen, fungal spores, molds, and bacteria, besides the influx of outdoor aerosols 

such as road and soil dust. 

Fine particulate matter caused air pollution has been considered in recent years as a serious 

environmental concern. Recent studies (Bruce et al., 2000; Smith, 2000) have found that the respiratory 

diseases such as TB, cardio-vascular diseases, COPD etc. are associated with the long-term exposure to 



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fine particulates. India has second highest per capita burden of respiratory illnesses in the world (Smith, 

2000). About one million deaths are reported annually in India due to household air pollution caused by 

fumes from cooking, heating and lighting activities. Till few years back, Mass Concentration (MC) of 

PM was used to assess the health impacts. Now we know other metrics such as Number Concentration 

(NC) and Surface Area Concentration (SAC) metrics are important as well and this is confirmed by the 

studies on cell cultures and animal respiratory system (Brown et al., 2001; Oberdorster, 2000; Tran et 

al., 2000; Heitbrank et al., 2009). We have already studied and reported the results on number and mass 

concentration in Indoors (Monkkonen et al., 2004). 

Of all the PMs, the fine particulates (0.01-2.5 µm) have a greater probability of delving deep into the 

lungs (Zhou & Cheng, 2000; Churg & Brauer, 2000). Ratio of surface area to mass becomes greater as 

we move towards small diameter particles. For a given mass, surface area increases as 1/diameter. 

Hence the necessity for expressing the regional dose in terms of surface area concentration in the 

epidemiology studies. Jicheng Gong et al. (2019) study shows that particle size ranges having large 

SAC and/or volume concentration have significant positive association with pulmonary inflammation. 

The recent USEPA particle size chosen as standard for epidemiological studies is PM2.5, which are 

particles smaller than 2.5 micrometer in mean aerodynamic diameter (USEPA; 1996; Schwartz et al., 

1996). In the (Sahu et al., 2011) study conducted in rural India, the correlation between PM2.5 and 

surface area concentration was found to be low to moderate. The study showed correlation coefficient 

R=0.38 in the tracheobronchial region and R=0.47 in the alveolar region. This indicates that PM2.5 is 

not a sufficient proxy for particle surface area concentration.   

However, to conform to the latest findings, new standard of PM2.5 is to be adopted for further study. 

The USEPA Clean Air Scientific Advisory Committee (CASAC) review (Wolff, 1996) suggested a 

24-hr PM2.5 standard in the range of 20 to 65 µg/m
3
. Many studies (Kamens et al., 1991; Lighty et al., 

2000) have indicated that cooking is the primary activity responsible for generating fine particles in 

Indoors. According to Kamens et al. (1991) study, particles below 1 µm are major constituent of the 

emission. The average diameter of these particles in the emission was around 0.8 µm. A recent study 

(Patel et al., 2020) has confirmed this where they found that NC was highest for sub-10 nm particles 

during cooking activity with kerosene stove.   

In such a study, some of the important tasks included are: measurement of exposure with a particular 

exposure metric, comparing them with the existing emission or exposure standard, understanding their 

impact on the exposed population, and also carrying out source-apportionment of the aerosols 

monitored. 

In India, many studies have been conducted on the health effects of biomass fuel usage (Smith, 1986). 

However, very few studies have compared the kerosene and Liquid Petroleum Gas LPG fuels which 

are also commonly used for household purposes. According to a recent study (Elf, Jessica L et al., 

2018), 57% of urban homes use LPG as the lone and primary fuel, whereas kerosene and wood is used 

as a secondary main fuel in about 22-25% of homes.  



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In India, especially in economically poorer section, women spend primary part of their time indoors 

cooking with crude fuels with poor ventilation and, also the stoves are inefficient. Kerosene is being 

used by economically lower-class people who live in poorly ventilated small houses. Studies in India 

have concentrated on the mass concentration of PM10 and PM5. In the present study SAC of fine 

particles are measured. We are going to compare the obtained data with respect to the type of fuel used 

indoors and as well as study their variation patterns.  

The present study is conducted in Mysore city situated in the state of Karnataka having average 

minimum temperature of 15
o
C and maximum temperature is 35

o
C. It is a small city with a population 

of 893.062 (Census of India 2011) and an area of more than 150 sq. km. Cooking activities using 

kerosene and LPG as main fuel with poor ventilation facility found in many of the houses.  

 

2. Method 

In the process of aerosol measurement, the first part is collecting a small sample from the bulk aerosol 

for analysis. The process is referred to as aspiration. These particles are transported to the measurement 

instrument. The particles are analyzed in real-time (dynamic measurement). Prior to the collection or 

analysis, an additional step called pre-classification is added to get an expected aerosol for sampling. 

For example, when we want to measure particles smaller than 10 µm, we have to remove all particles 

of size larger than 10 µm before collecting the sample.  

2.1 Instrument Used 

Diffusion Charger (e.g., LQ1-DC, Matter Engineering, AG) 

The technology necessary to measure aerosol exposure in terms of SAC is not widely available. In the 

present study we are going to use LQ1-DC (Matter Engineering, Switzerland), to directly measure active 

surface area by the method of diffusion charging. 

 

 

Figure 1. Mechanism of Unipolar Diffusion Charging  

Source: www.matter-engineering.com 

 



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The unipolar diffusion charging mechanism used is shown in Figure 1. A small positive voltage of 

approx. 20 V DC is applied to the grid. Aerosols in the gas stream are guided between the grid and the 

grounded tube. With the corona discharge from the corona tip to the grid, ions get produced in the 

carrier gas. These ions attach to the surface of the aerosol particles. The particles carrying positive 

charge flow to the sensor unit and are absorbed in the measuring filter. The filter is connected to a ultra 

low current amplifier (resolution 1 fA=10
 –15

 A). The voltage signal from the charge amplifier is 

converted to the calibrated analogue signal 0…5 V DC. This signal is displayed as 0…1000 after 

adjustment.  

The LQ1-DC instrument measures the integral attachment cross section of ions on particles. It is the 

part of the geometrical surface which is directly accessible from outside; hence the use of term “active 

surface” or “Fuchs surface area”. The measurement gives the total active surface area per unit volume. 

To overcome multiple charging for particles larger than 10 nm, the in concentration is kept low. Due to 

coulomb repulsion, the ion attachment rate in case of multiple charging decreases.    

We now discuss the technical specifications, pre-sampling set-up, of the instrument LQ1-DC used for 

measuring the SAC. 

 

 

Figure 2. LQ1-DC Instrument to Measure Surface Area Concentration 

 

Data logging time-interval for Indoor measurements was set to 10 sec. Before each sampling, Electrical 

Zero is adjusted to zero using potentiometer.  

Response time: few seconds 

Particle size range: few nm to 2µm.   

 Resolution: 1 µm
2
/cm 

3
 

 Gas flow rate: approx. 1.5 l/min 

2.2 Sampling Procedures 

The study is carried out in the city of Mysore in different households using either kerosene or LPG fuel 

for cooking. Five houses of each type were randomly selected from different parts of the city. Kerosene 

and LPG houses were chosen in pairs such that a kerosene house lied in an area close to a LPG house. 



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It was also noted during the selection of the houses that the households also used some other fuels 

wood, dung cakes etc. All measurements were carried out in the period from August to January.  

On the day of sampling, the LQ1-DC instrument was taken to the sampling location and were run by 

battery for 24-hr to collect the real-time SAC data. The instrument was kept in the living room at a 

height of about 50 cm from the floor.  

Main activities within households including cooking were noted down by the family member along 

with the corresponding timings. After a gap of 4 months, measurements were repeated for the second 

time in each house. The sampled data stored in the memory unit were downloaded to the PC from the 

instruments after each sampling. 

2.3 Analysis of the Data 

Statistical analysis of 1-minute-average time-aligned data file was made using Microsoft Excel 

software. Statistical entities like the arithmetic or geometric mean averages, standard deviation and 

variance were calculated from data files of each sampling site using the functions provided in the 

software. These results were used to make comparisons between the two types of environments. 

Also, variation aspect of the metric data was studied with these results. The two types of sampling 

categories were checked for statistical differences in terms of metric concentrations.  

To find statistical difference, following procedure was used. First, the mean and standard error of 

the surface area concentrations of two groups, e.g., kerosene and LPG houses were obtained. Let 

the number of houses for kerosene and LPG groups are N1 and N2 respectively. The standard error 

for each group, e.g., kerosene group is obtained as  

𝑆𝐸 =  
𝑆𝑡𝑑.𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛

𝑠𝑞𝑢𝑎𝑟𝑒 𝑟𝑜𝑜𝑡 𝑜𝑓 (𝑁)
 

The two groups are statistically different if their error bars do not overlap. For example, let the 

mean and standard error for kerosene be m1 and se1, and those for LPG be m2 and se2. Then if 

(m1-se1) > (m2+se2), (i.e., the lower bound for kerosene is greater than the upper bound for LPG) 

then, the two groups are statistically different. Otherwise, the two groups are not different.  

The statistical results of the analysis in the present study are presented in the next section in terms of 

averages and standard deviation representing the particular environmental category. Standard deviation 

actually gives the amount of dispersion involved in the metric levels. From the variation levels, we can 

know if any significant change in concentration levels occurs over a day, or over different sampling sites. 

 

3. Results and Discussion  

In this section, we will discuss analysis of the data collected to know the particular association of 

some of the sources with concentration levels, the daily variation pattern, and the average SAC 

concentration levels for the two indoor categories and comparison between them.  

 

 



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3.1 Association of Peaks with Activities. 

The data collected from the LQ1-DC were mapped with the activities recorded by the housewife in the 

activity log sheet. Following associations were found in general. 

Cooking Activities identified with the peaks 

i. Heating water for bath: Time: Morning 6 to 9 am, A high unique peak 

ii. Breakfast: Time: morning 7-9 am  

iii. Meals: Time: morning 11-12 noon, A moderate peak 

iv. Evening cooking: Time 5-7 pm, Moderate peaks 

v. Late night: 11 pm-4 am, steep decrease in concentrations reaching minimum levels. 

Along with the cooking fuel, the household were found to use alternate fuels such as coconut husk or 

dung cakes. The unique high peaks observed in surface area concentration corresponded to this activity 

of heating water for bath noted down in the activity log sheet which usually occurred in the morning 

hours from 6 am-9 a.m. (see Figures 3 and 4). This is a major aerosol generating activity and usually 

would last for 2-3 hours. 

Other peaks during the day also corresponded to the activities noted down in the activity log sheet. All 

other peaks afterwards are found to be associated with the household activities mainly the cooking 

processes, such as preparing tea, breakfast, meals, etc. So, to further understand the association of 

aerosol properties with the activities, it is necessary to study each of the peaks relating to the particular 

activities in detail. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3. Indoor SAC Variation over 24-Hours 

 

 

1.00E+07

1.00E+08

1.00E+09

1.00E+10

8:38 13:26 18:14 23:02 3:50 8:38

su
rf

a
c
e
 a

re
a
 c

o
n
c
. 

time (hrs)

Indoor S.A. Concentration, µm2/m3



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3.2 Variation Pattern over a Day 

The SAC variation pattern was similar across the different sampling locations, except during 

combustion activities. Corresponding to the each particular combustion activity, fuel-specific particle 

emission takes place influencing the concentrations as well as the size ranges. There was difference in 

the nature of the peaks from one activity to another which was evident through the graphical plots of 

the concentration as well as the activity log sheet. This corresponds to the source-specific nature of the 

aerosols generated. After constant low levels of concentrations in the early hours of the day, sharp 

peaks are seen from around 5 a.m. to 9 a.m. due to the inflow of particles onset by activities and wind 

flow. Moreover, after around 6 am to 9 am, combustion activities start to take place. These usually 

include heating water for bath, preparing morning breakfast, and even sweeping.  

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4. Indoor SAC Variation over 24-Hours 

 

Usually, the afternoon period of 12-4 pm was found to be little calmer durations with lower 

concentration levels. Concentrations rise to moderate levels from around 5 pm-7 pm and variation 

pattern is similar to early morning hours, but with little lower concentration levels. This is found to be 

due to the increase in outdoor activities like movement of vehicles, or due to smaller-duration cooking 

activities which included preparing tea, snacks, and sometimes dinner also. In the late-night hours, after 

about 11 pm, there was a steep decrease in concentrations reaching a minimum and remaining constant 

at that level till around 4-5 am. After 11 pm, family members are taking rest; hence there is no 

disturbance causing the coarser particles to gradually settle down. No unique difference in variation 

was found during this period between Kerosene and LPG houses. 

1.00E+07

2.10E+08

4.10E+08

6.10E+08

8.10E+08

1.01E+09

1.21E+09

21:00 23:05 1:10 3:15 5:20 7:25 9:30 11:35 13:40

S
u
rf

a
c
e
 a

re
a
 c

o
n
c
. 

time (hrs)

S.A. Concentration in  µm2/m3



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Nighttime average values of concentration during 12 p.m.-4 a.m. are considered as background 

concentration levels. Since it is no-activity period, there will be fewer disturbances for aerosol 

movements.   

We can refer to results from other studies in this. Study by (Kamens et al., 1991) showed that at 

midnight, when everyone in the house had gone to sleep, aerosol concentration declined indicating the 

settling of larger particles. Our study also showed similar results.  

The activity log-sheet was helpful to know the cause for unusual variations in concentration levels. The 

plot shown above (Figure 3 and Figure 4) represents an indoor daily variation with a sudden peak 

around midnight. The sudden peak is because of the doors being opened when one of the house 

members came late in the night, which might have caused the wind in-flow, resulting in random 

movement of the settled particles. It can also be observed from the plot that later on, the concentration 

levels remained higher than the usual levels throughout the night, with a small gradual decrease in 

concentration. Study by (Kamens et al., 1991) has showed that the fine particle levels after a peak 

would remain high for a longer period. 

For urban locations, within-day variation was found to be about 2-3 times higher than between-day 

variation. But for remote locations, not so high difference was found between the two, indicating 

the influence of lesser sources and activities.  

3.3 Concentration Levels of the Metric 

Data downloaded from the LQ1-DC after each sampling was 1-min average data of SAC. From 1-min 

average data, statistical results such as Arithmetic mean, Geometric mean, and Standard Deviation etc. 

were obtained for all Indoor sites. 

Table 1 gives the summary of average concentration, their standard deviation for Indoor measurements. 

LPG house SAC concentrations are little lower compared to the kerosene households. 

One of the kerosene houses sampled was in a slum area, with poor ventilation facility within the house; 

the influence from neighboring houses also was large, because of too much congestion. In two other 

kerosene houses, the neighboring house influence on the concentration was large. One kerosene and 

one LPG house had dusty surrounding environment. Statistical difference was found between the two 

groups for SAC as the exposure metric. Variance among the households is nearly the same among the 

two groups. 

 

 

 

 

 

 

 

 



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Table 1. 24-hr Average Data of the Metrics from Indoor Measurements with Kerosene and LPG 

as the Main Fuel 

House ID 

Kerosene house LPG house 

Metric values in each house Metric values in each house 

Surface area conc. (0.01-2.0) 

µm
2
/m

3
 

Surface area conc. (0.01-2.0) 

µm
2
/m

3
 

1 1.63E+08 1.49E+08 

2 4.30E+08 

 6 2.99E+08 3.45E+08 

7 7.79E+08 

 8 

  9 

 

4.31E+08 

10 7.76E+08 

 11 3.77E+08 1.37E+08 

S.D. (µg/m
3
) 55.34 46.31 

Variance (%) 54.54 62 

 

Standard Error (S.E.) is found out for the two groups using S.D. from Table 1, using the formula 

discussed earlier. Later, we find that Kerosene (Mean-SE) > LPG (Mean+SE) indicating that the two 

indoor categories, viz., kerosene and LPG households are statistically different. 

Table 2 shows the ratio of concentrations found in two categories of houses is 1.79 indicating that 

usage of kerosene fuel is leading to higher concentrations than LPG. Thus LPG is a cleaner fuel than 

kerosene.  

 

Table. 2. Indoor Measurements in Houses: 24-hr A.M. Values of Surface Area Concentration 

(0.01-2 µm) (n=5) (Background indoor pollution: 36+16 µg/m
3
) 

Kerosene 

house 

LPG 

house 

Kerosene/LPG ratio: 

range and G.M. 

(4.71+2.54)E+08 

µm
2
/m

3
 

(2.65+1.46)E+08 

µm
2
/m

3
 

1.76 to 1.82 

GM=1.790 

 

Considering both groups of houses in general, for surface area concentration, the ratio of 24-hr mean to 

background value for each house is found to vary from 1.6 to 3 with an average of 2.5. This shows the 

influence of the combustion activities which increases the PM concentration to more than two times the 

normal levels. For instance, in one kerosene house, SAC was found to exceed 2000 µm
2
/cm

3
 two times 



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in a day. These levels are potentially harmful to the inhabitants who get exposed to them on a daily 

basis.  

The ratio of peak value to 24-hr mean value for each house is found to vary from 4 to 66, with an 

average of 21.2. This shows the impact of emissions during combustion activities to increase the PM 

concentrations from normal levels to more than 21 times. This indicates how some combustion 

activities can create really high dose of short-term particulate exposure to the inhabitants. Ventilation 

facility in the house influences the concentration levels very much. Results from earlier studies have 

also shown the dependence of PM2.5 exposures on the ventilation levels within the houses (Sarnat et al., 

2000). In some cases, the outdoor sources had a major effect on the exposure levels indoors. 

 

4. Conclusion 

National Ambient Air Quality standards (NAAQS, 2009) in India stipulate standards for PM2.5 at an 

annual average of 40 µg/m
3
 and 24-hr average of 60 µg/m

3
. However, there are no standards specified 

for SAC. 

Houses using kerosene fuel for cooking were found to be more (about 1.79 times) polluted with 

particulate matter than the houses using LPG. Every cooking activity resulted in concentration peaks. 

Indoor peak values were seen to be about 20 times higher than the 24-hr mean values. Indoor average 

concentrations exceeded background values by more than two times. In kerosene-using houses, generally 

poor ventilation facility and congestion was found. Heating water for bath during early morning hours 

was one major aerosol generating activity which lasted for 2-3 hours. Peaks during heating water for bath 

in each house were found to far exceed the peaks during the other activities. This indicates the influence 

of the crude fuels like wood, dung cakes etc. Similar peak was found daily during evening times between 

5 p.m. and 7 p.m., mostly due to the increase in outdoor activities, and penetration of outdoor particles 

into the houses. For kerosene and LPG fuel types, not much difference in daily variation pattern was 

found.  

In the light of health effects associated with aerosol exposure, results of the present study indicate 

alarmingly high exposure to household women who spend majority of their time indoors doing cooking. 

There is a risk of high-dose of short-term exposure to the inhabitants. There is the need to ensure that 

their exposure to particulate matter is well within the permissible limits for a healthy living. May be 

good ventilation facility within living room and kitchen, cleaner fuel and efficient stoves etc., can 

lessen the exposure levels to these particulates generated. Also, the use of kerosene fuel, wood and 

dung cakes etc., will have to be replaced by better fuels like Liquid Petroleum Gas (LPG).  

The latest survey released by the National Sample Survey Organization (NSSO, 2012) has revealed that 

in rural areas, subsidized kerosene was used in less than 1% of kitchens, which relied largely on 

firewood and chips as the primary source of energy for cooking during 2011-12. More than 67% of 

rural households in India still depend on firewood or wood chips for cooking. In contrast, around 68% 

of urban households use LPG for cooking. When it comes to lighting homes in villages, the share of 



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kerosene is estimated at 26.5%, with electricity's share estimated at 72.7%. In urban India, 3.2% of 

households use kerosene for lighting while the share of electricity is 96%. 

The encouraging aspect in urban households is that the use of kerosene has declined from more than 23% 

to 6%. Consumption of kerosene, used primarily for lighting and cooking purposes in rural areas, has 

dropped by a sharp 21% in 2016-17 from a year ago aided by greater use of cleaner Liquefied Petroleum 

Gas (LPG) for cooking and coverage of more villages under the rural electrification program, as per data 

from Petroleum Planning and Analysis Cell, an arm of the oil ministry. In the same period, consumption 

of LPG jumped by 9.8%. In 2016-2017, there were issued a total 3.25 crore new LPG connections, the 

highest number of connections given in any year ever. This number included the 2 crore connections 

given under the “LPG-for poor women” scheme, the Pradhan Mantri Ujjwala Yojana (PMUY). India’s 

fossil fuel consumption trend is suggesting a shift away from inefficient and highly polluting use of 

hydrocarbons, as a result of efforts to move towards a less-carbon-intensive economy. Recent WHO 

pollution report mentions this scheme saying “India’s PMUY Scheme has provided some 37 million 

women living below the poverty line with free LPG connections to support them to switch to clean 

household energy use” (WHO, 2018). Worth mentioning also are some of the non-government 

organizations such as Confederation of Indian Industry (CII) which has established the Indian Green 

Building Council (IGBC) and working closely with state governments and central government in 

promoting green building concepts in India (https://igbc.in/). 

The present study has setup a database for SAC metric at two Indoor environments in a medium Indian 

city and initiated a study of the association of peaks with cooking activities and the variation pattern of 

the metric. This kind of study may become more satisfactory with the rigorous study of peaks 

associated with combustion activities, as to the particle size range, density or morphology of particles 

generated by different sources. 

 

Acknowledgement 

The author is grateful to Dr. Andrew Maynard and the University of Mysore which gave permission to 

use the instrumentation facility mentioned in the article.  

 

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