Indoor Air Quality Pollutants: Analysis of Different Environments

This report investigates indoor air quality by analyzing pollutant levels in different environments. The study focuses on measuring levels of nitrogen dioxide (NO2), carbon dioxide (CO2), PM2.5, temperature, and relative humidity across three sites. Data was collected using an IAQ system and analyzed using descriptive and inferential statistics, including t-tests. The findings reveal significant differences in NO2 and CO2 levels between the sites, highlighting variations in indoor air components based on location. The report discusses the mean values, ranges, and correlations of the pollutants, comparing them to national standards and identifying potential hazards. Control measures, such as proper ventilation and the use of natural cleaners, are recommended to improve indoor air quality. The study concludes that indoor air quality components and pollutants vary depending on building conditions, emphasizing the need for monitoring and control measures for a healthy indoor environment.

Running head: INDOOR AIR QUALITY POLLUTANTS 1
Indoor Air Quality Pollutants
Student name:
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Measuring Pollutant Levels of Indoor Air Quality in Different Environments
Abstract
Indoor surroundings are associated with the presence of different sources of pollution.
Furthermore, the majority of individuals presently spend almost 90% and above of their within
buildings. The indoor air quality is, therefore, a critical factor that must be controlled to give
occupants a comfortably healthy stay. Within rooms, different components exist some of which
are pollutants when in excess. These include; nitrogen dioxide (NO2), Carbon (IV) oxide (CO2),
PM2.5, temperature, carbon monoxide, temperature, particulate matter, and relative humidity
among others (FMlink, 2020). To monitor these, different systems are used among them being
the IAQ-a system that applies a cheaper wireless network sensor to gather information on indoor
air quality. The IAQ system stores and avail monitoring of data in real-time.
Results on these parameters are analyzed in Microsoft Office Excel 2013to show the
relationship in means for different parameters under diverse environments. Likewise, testing for
the same group parameter correlation is conducted. Further descriptive statistics are also
conducted on the parameters to show the difference mean, standard deviation and ranges in the
three locations. From the results, the indoor air components at some point tend to be different
based on the location and conditions within the room.

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Introduction
IAQ (Indoor Air Quality) is the quality of air that exists within structures and buildings,
more so in matters relating to the comfort and health of the occupants (EPA, 2019). People have
always suffered unknowingly from the effect of poor quality indoor air. Problems such as
headache, fatigue, sinus congestion, coughing, dizziness, nausea, breathing shortness, eye, throat,
skin and nose irritation are often experienced (CCOHS, 2020). Pollutant sources like tobacco
smoking, fuel-burning, building materials, household cleaning and maintenance products, central
heating and systems of cooling, excess moisture, and outdoor pollution are the sure causes of
indoor air pollution (EPA, 2019). This makes the study therefore important as it reveals the level
of indoor air pollution content making it easier for house owners to find necessary means of
making and maintaining the indoor air of quality (Testo, 2020).
To conduct the research, there is a need for determining site and sensor locations. Site
location, in this case, refers to a building/structure that is used for collecting the indoor air
quality data using the IAQ system. On the other hand, sensor locations refer to the place/area
where the sensor transmitters are located for collecting data on the indoor parameters.
The study is set to investigate whether there exists significant differences in the NO2 and
CO2 levels for the three sites.
Research Questions
1. Is there significant difference in the NO2 levels for the different sites?
2. Is there significant difference in the CO2 levels for the different sites?

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Research Hypothesis
1. Null hypothesis (H0): There is no significant difference in the NO2 levels for the different
sites.
Alternative hypothesis (HA): There is significant difference in the NO2 levels for the
different sites .
2. Null hypothesis (H0): There is no significant difference in the CO2 levels for the different
sites.
Alternative hypothesis (HA): There is significant difference in the CO2 levels for the
different sites.
Methodology
An initial visit was conducted in various places where the study areas were randomly selected.
Monitoring locations were then selected and the study areas verified. The equipment was
prepared, set up and calibrated in these three sites ready for data collection. Moreover, the
characterization of buildings used for the study was conducted.
Site assessment
The Building Assessment Survey and Evaluation Study, a study that covered two public office
structures and one private office structures within New York City, USA, was done.
Environmental pollutants and comfort parameters were then recorded at different time intervals.
Additionally, ventilation, air-conditioning, and heating were recorded too.

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Pollutant source assessment
Internal pollutants such as materials used in the house, type of energy used for cooking, tobacco
smoking, and central cooling systems among others were checked and assessed through The
Pollution Source Assessment Tool (PSAT) (WaterNSW, 2017). This gave information about the
possible causes of the different pollutant levels within the selected structures. Moreover, it made
it easier for pollution risk conclusions as it gave prior knowledge of the possible causes of
pollution before the analysis was conducted.
Data assessment
The collected data was then cleaned where unwanted information was removed. Data were then
sorted according to the area of collection. Finally, an analysis was conducted on this data and the
results displayed. Both descriptive and inferential analysis was undertaken.
Results
Descriptive Statistics
Table 1. Descriptive statistics for ESB 02 parameters
NO2
(ppb) CO2 PPM PM2.5 (μg/m³) TEMP (°C) RH (%)
Mean 25.68 473.98 3.46 21.43 58.45
Standard Error 0.52 1.62 0.02 0.08 0.35
Median 24.74 473.00 3.45 21.46 60.08
Mode 23.72 410.00 3.36 20.38 54.33
Standard Deviation 13.96 43.78 0.61 2.03 9.56
Sample Variance 194.96 1916.39 0.37 4.10 91.33
Range 71.69 152.00 2.98 11.22 58.13
Minimum 0.03 398.00 2.04 18.69 26.91
Maximum 71.73 550.00 5.03 29.91 85.04
In this case, NO2 has a mean of 25.68 ( SD=13.96), CO2 a mean of 473.97 (SD=43.78), PM2.5
a mean of 3.46(SD=0.61), Temp a mean of 21.43, ( SD=2.03) and RH (%) a mean of 58.45,

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(SD=9.56). The NO2 range is 71.69, CO2 a range of 152,(SE=1.62), PM2.5 a range of 2.98,
(SE=0.02), Temp a range of 11.22, SE=0.08 ¿, RH(%) a range of 58.13, ( SE=0.35).
T-test
Testing differences in NO2 for ESB.02.156 B & ESB.04.168 B
H0 : μ1=μ2
H1 : μ1 ≠ μ2
Table 2: t-Test: Two-Sample Assuming Unequal Variances
ESB.02.156
B ESB.04.168 B
Mean 25.68296602 18.83550275
Variance 194.95911 101.0162718
t Stat 10.72433142
P(T<=t) one-tail 4.37973E-26
t Critical one-tail 1.646011446
P(T<=t) two-tail 8.75947E-26
t Critical two-tail 1.961766882
From the table above, p-value is 0.000 (a value less than 5% level of significance), we therefore
reject the null hypothesis and conclude that there is significant difference in the mean NO2 for
the two sites.
Testing differences in NO2 for NO2 in ESB.04.168 B & ESB.11.248 B
H0 : μ1=μ2
H1 : μ1 ≠ μ2
From the table
above, p-value is
0.840 (a value
Table 3: t-Test: Two-Sample Assuming Unequal Variances
ESB.04.168 B ESB.11.248 B
Mean 18.83550275 18.74901
Variance 101.0162718 32.67958
t Stat 0.201546254
P(T<=t) one-tail 0.420153555
t Critical one-tail 1.646179718
P(T<=t) two-tail 0.84030711
t Critical two-tail 1.962028965

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greater than 5% level of significance), we therefore fail to reject the null hypothesis and conclude
that there is no significant difference in the mean NO2 for the two sites.
Testing differences for CO2 in ESB.04.168 B & ESB.11.248 B
H0 : μ1=μ2
H1 : μ1 ≠ μ2
Table 4: t-Test: Two-Sample Assuming Unequal Variances
ESB.02.156 B ESB.04.168 B
Mean 473.9752066 548.553851
Variance 1916.385591 62694.6318
t Stat -7.900217914
P(T<=t) one-tail 4.8204E-15
t Critical one-tail 1.646840111
P(T<=t) two-tail 9.64079E-15
t Critical two-tail 1.96305767
From the table above, p-value is 0.000 (a value less than 5% level of significance), we therefore
reject the null hypothesis and conclude that there is significant difference in the mean CO2 for
the two sites.
Testing differences for CO2 in ESB.04.168 B & ESB.11.248 B
H0 : μ1=μ2
H1 : μ1 ≠ μ2
Table 5: t-Test: Two-Sample Assuming Unequal Variances
ESB.04.168 B
ESB.11.248
B
Mean 548.5538511 451.5604339
Variance 62694.63179 5850.372183
t Stat 9.975826582
P(T<=t) one-tail 1.49828E-22
t Critical one-tail 1.646631512
P(T<=t) two-tail 2.99656E-22
t Critical two-tail 1.962732708

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From the table above, p-value is 0.000 (a value less than 5% level of significance), we therefore
reject the null hypothesis and conclude that there is significant difference in the mean CO2 for
the two sites.
Discussion
Mean values acquired from the analysis show the pollutants' content levels discussed in
the introduction. The test of the relationship between parameters in the three setups shows the
difference in indoor air component quantities. Moreover, the seven hypothesis statements were
all answered. A test on descriptive statistics has shown that there is a difference in the mean, and
standard deviation for NO2, CO2, temperature, relative humidity, and PM2.5 in ESB 2, 4 & 11.
The test further shows that the range in the parameter values in these three structures differs
showing that their conditions were different. Moreover, inferential analysis tests conducted
proved the relationship between parameters in the three structures. In all the groups, there is a
negative correlation between NO2 and relative humidity. Group one expresses very weak
correlations among variables followed by ESB4 while in ESB 11, parameters have between
weak, average and strong correlations. This further confirms the previous statement that the
conditions between the three groups are different. NO2 in ESB 2 and ESB 4 are found to have
close means and are different from the ESB11 NO2 mean. This shows that the conditions in
structure ESB 11 are different from the ones in ESB 2 and ESB 4. T-tests on Carbon (IV) oxide
showed that there is no significant difference in its mean across the three structures. Further test
on PM2.5 showed that there is no significant difference in the means of the parameter in the
three conditions. This shows that PM2.5-affecting conditions were almost the same for the three
structures. Likewise, the ANOVA test on indoor temperature showed that there is no significant

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difference in the mean of ESB 2, 4 and 11 temperatures. Like in PM2.5, temperature-affecting
factors in these three structures are almost the same.
Comparing the values to the national standards, the mean values of relative humidity for
the structures (58.45% for ESB2, 60.64% for ESB 4 and 65.52% for ESB 11) are higher than the
standard value which is between 40-50%. For temperatures, the mean values from the three
conditions (21.43 for ESB2, 20.00 for ESB 4 and 18.53 for ESB11) are almost the same as the
standard temperature during winter (20-25.5). However, these values are slightly lower than the
summer standard values that range from 23 to 28 degrees. The particulate matter (PM 2.5) values
in the data (3.46 for ESB2, 3.14 for ESB 4 and 3.01 for ESB 11) are lower than the set standard
showing that these structures are less polluted by these particles. The mean values of CO2
(473.98 for ESB 2, 548.55 for ESB 4 and 451. 56 for ESB 11) are within the standard range
which is 400-1000ppm. Finally, NO2 averages (25.68 for ESB 2, 18.84 for ESB 4 and 18.75 for
ESB 11) are than the standard range (Bonino, 2016).
Control measures such as proper ventilation, avoiding smoking near home, preventing
and regulating pests, eradicating odors rather than sealing them, using natural cleaners and
crafting projects in spaces that are well ventilated (School, 2018).
In the end, the indoor air quality components and pollutants are different depending on
building/structure conditions. Moreover, there is a difference in the number of pollutant
parameters based on environments of research. For a healthy and comfortable indoor stay, one
should consider the control measures mentioned above among others both in residential and
office structures.

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