Abstract

This study evaluated light (lux) and sound (dBA, dBC) conditions across four locations within the IMSE building. Measurements were collected using Lux Meter and NIOSH Sound Level Meter apps, with three replications per condition. Statistical analysis in R included descriptive statistics, two-sample t-tests, and ANOVA for comparing light and sound levels across locations. Results indicate significant variation between environments, with certain locations offering better lighting but higher noise, while others were quieter but less illuminated. The findings emphasize the importance of optimizing both visual and auditory conditions in workspaces to support productivity and comfort.

Background and Relevance

Lighting and sound conditions in a workspace strongly influence comfort, health, and productivity. Poor lighting can lead to eye strain, fatigue, and reduced accuracy in tasks, while excessive noise exposure contributes to stress, distraction, and even long-term hearing issues.
In academic environments, appropriate illumination and quiet study areas are critical for focus and learning efficiency. The goal of this lab was to measure and analyze lighting and sound in selected IMSE locations, identifying their strengths and weaknesses as study/work environments.

Literature Review

Source 1: IESNA Lighting Standards
The Illuminating Engineering Society of North America (IESNA) recommends 300–500 lux for reading and office work. Excessive lighting may cause glare and discomfort, while insufficient lighting hampers concentration.

Source 2: OSHA/NIOSH Noise Guidelines
The Occupational Safety and Health Administration (OSHA) specifies permissible exposure limits for noise, typically 90 dBA for an 8-hour shift, with NIOSH recommending stricter limits (85 dBA). Even lower noise levels can disrupt concentration during cognitively demanding tasks.

Methods

Measurements were taken at four campus locations: Lab 240, IMSE hallway, Livermore Center (1), and Livermore Center (2). Lux and sound levels were recorded three times per subcondition (e.g., low vs. maximum light; high vs. low frequency sound).

Results

Data Analysis

Below are the descriptive statistics and plots for both light and sound levels.

  • Locations were ranked by mean light levels and mean sound levels.
  • Standard deviations indicate variability across measurements.
  • Outlier detection confirmed that values were within expected range compared to class data.
  • The t-test was used to compare two specific conditions (e.g., high vs. low frequency in Lab 240).
    It helps determine whether the observed difference in mean sound/light levels between two conditions is statistically significant or just due to random variation.
  • The ANOVA (Analysis of Variance) was applied when comparing measurements across more than two locations.
    It tests whether at least one location’s mean value differs significantly from the others, providing a broader comparison than the t-test.
  • The boxplots visually represent the distribution of light and sound levels at each location.
    They highlight medians, variability (IQR), and possible outliers, making it easier to compare environments and spot locations with unusual conditions.

Interpretation of Variability

Standard deviation (SD) provides a measure of variability across repeated measurements.
- Locations with low SD indicate more consistent conditions (e.g., stable lighting or steady background sound).
- Locations with high SD suggest fluctuating environments (e.g., intermittent noise, changing light sources).

For example: Lab 240 showed relatively low SD in light measurements, meaning lighting was stable across repetitions.
- The hallway and Livermore Center areas had higher SD in sound levels, reflecting variability due to foot traffic and surrounding activities.

This variability is important because it indicates how predictable and reliable each environment is for sustained tasks. Even if a location’s mean light or sound is acceptable, a high SD may reduce comfort and concentration due to frequent fluctuations.

Analytical Introduction

At the beginning of this analysis, we recorded and compared both sound levels and light intensity across four different locations.

• For Location 1 (Lab 240), sound was measured under high and low frequency sub conditions (dBA and dBC), and light was measured under low and maximum light conditions.

• For Locations 2, 3, and 4, only dBA (sound) and lux (light) readings were recorded, each with three repetitions.

This report evaluates how frequency, environmental setting, and lighting conditions affect both sound levels and light intensity, using statistical tools (t-tests and ANOVA) supported by visualizations.

Location wise Sound & Noise Level analysis

We measured sound levels (dBA and dBC) at four locations. In Lab 240, measurements were taken under High Frequency and Low Frequency sub conditions, allowing for two-sample t-tests. For Locations 2, 3, and 4, only dBA values were recorded and compared using ANOVA.

Location 1: High vs Low Frequency

Data Input

data1 <- data.frame(
  Location_ID = rep(1, 6),
  Location_Name = rep("Location 1 (Lab 240)", 6),
  Subcondition = c(rep("High frequency",3), rep("Low frequency",3)),
  dBA = c(80.5, 91, 84.3, 48.3, 48.6, 49.5),
  dBC = c(80.8, 91.6, 93.2, 72.4, 73, 73.3)
)
data1
##   Location_ID        Location_Name   Subcondition  dBA  dBC
## 1           1 Location 1 (Lab 240) High frequency 80.5 80.8
## 2           1 Location 1 (Lab 240) High frequency 91.0 91.6
## 3           1 Location 1 (Lab 240) High frequency 84.3 93.2
## 4           1 Location 1 (Lab 240)  Low frequency 48.3 72.4
## 5           1 Location 1 (Lab 240)  Low frequency 48.6 73.0
## 6           1 Location 1 (Lab 240)  Low frequency 49.5 73.3

High Frequency Condition

  • Range of dBA: 80.5 – 91.0
  • Range of dBC: 80.8 – 93.2
    Visually, both dBA and dBC values are close in range under high frequency.

Statistical Test

high <- subset(data1, Subcondition == "High frequency")
t_high <- t.test(high$dBA, high$dBC, var.equal = FALSE)
t_high
## 
##  Welch Two Sample t-test
## 
## data:  high$dBA and high$dBC
## t = -0.65882, df = 3.793, p-value = 0.5479
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -17.33475  10.80142
## sample estimates:
## mean of x mean of y 
##  85.26667  88.53333

Visualization

boxplot(high$dBA, high$dBC, names = c("dBA High","dBC High"),
        main = "High Frequency Comparison", ylab="Sound Level (dB)", col=c( "lightblue", "red"))

Interpretation: Since p-value > 0.05, there is no statistically significant difference between dBA and dBC at high frequency. Both follow a similar trend.

Low Frequency Condition

  • Range of dBA: 48.3 – 49.5
  • Range of dBC: 72.4 – 73.3

Here, dBC values are consistently much higher than dBA.

Statistical Test

low <- subset(data1, Subcondition == "Low frequency")
t_low <- t.test(low$dBA, low$dBC, var.equal = FALSE)
t_low
## 
##  Welch Two Sample t-test
## 
## data:  low$dBA and low$dBC
## t = -53.889, df = 3.6697, p-value = 1.868e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -25.387 -22.813
## sample estimates:
## mean of x mean of y 
##      48.8      72.9

Visualization

boxplot(low$dBA, low$dBC, names = c("dBA Low","dBC Low"),
        main = "Low Frequency Comparison", ylab="Sound Level (dB)", col= c("lightblue", "red"))

Standard Deviation (SD) Comparison between High and Low Frequency

aggregate(cbind(dBA, dBC) ~ Subcondition, data=data1, sd)
##     Subcondition       dBA       dBC
## 1 High frequency 5.3163271 6.7448746
## 2  Low frequency 0.6244998 0.4582576

Interpretation:

  • Since p-value << 0.05, there is a highly significant difference between dBA and dBC at low frequency. dBC remains much higher compared to dBA.

  • dBA SD = 5.31, dBC SD = 6.74 → The sound readings fluctuate quite a lot. Noise levels are less stable (more variation between repeated measurements).

  • Low frequency (Lab 240):

  • dBA SD = 0.62, dBC SD = 0.46 → The sound readings are very consistent. Almost all repeated measures are close to the mean.

Locations 2–4: ANOVA on dBA

We further extended the study to three additional areas where only dBA readings were taken. Each location had three repeated measurements.

Data Input

data2 <- data.frame(
  Location_ID = c(2,2,2,3,3,3,4,4,4),
  Location_Name = c(rep("Location 2 (IMSE Hallway)",3),
                    rep("Location 3 (Livermore center 1)",3),
                    rep("Location 4 (Livermore center 2)",3)),
  dBA = c(40.8, 43.2, 41.0, 32.7, 33.1, 34.2, 35.5, 36.7, 37.2)
)
data2
##   Location_ID                   Location_Name  dBA
## 1           2       Location 2 (IMSE Hallway) 40.8
## 2           2       Location 2 (IMSE Hallway) 43.2
## 3           2       Location 2 (IMSE Hallway) 41.0
## 4           3 Location 3 (Livermore center 1) 32.7
## 5           3 Location 3 (Livermore center 1) 33.1
## 6           3 Location 3 (Livermore center 1) 34.2
## 7           4 Location 4 (Livermore center 2) 35.5
## 8           4 Location 4 (Livermore center 2) 36.7
## 9           4 Location 4 (Livermore center 2) 37.2

Location Ranges

aggregate(dBA ~ Location_Name, data = data2,
          FUN = function(x) c(min=min(x), max=max(x), mean=mean(x)))
##                     Location_Name  dBA.min  dBA.max dBA.mean
## 1       Location 2 (IMSE Hallway) 40.80000 43.20000 41.66667
## 2 Location 3 (Livermore center 1) 32.70000 34.20000 33.33333
## 3 Location 4 (Livermore center 2) 35.50000 37.20000 36.46667
  • Location 2 (IMSE Hallway): dBA ~ 40.8–43.2 (mean ≈ 41.7)
  • Location 3 (Livermore Center 1): dBA ~ 32.7–34.2 (mean ≈ 33.3)
  • Location 4 (Livermore Center 2): dBA ~ 35.5–37.2 (mean ≈ 36.5)

ANOVA Test

anova_result <- aov(dBA ~ Location_Name, data=data2)
summary(anova_result)
##               Df Sum Sq Mean Sq F value   Pr(>F)    
## Location_Name  2 106.30   53.15   50.78 0.000174 ***
## Residuals      6   6.28    1.05                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation: Since p-value << 0.05, there is a statistically significant difference in mean dBA levels across the three locations.

Visualization

boxplot(dBA ~ Location_Name, data=data2,
        main="Comparison of dBA across Locations 2, 3, and 4",
        ylab="Sound Level (dBA)", col="lightblue")

  • Hallway has the highest average sound level.
  • Livermore Center 1 has the lowest noise.
  • Livermore Center 2 falls in between.

Standard Deviation (SD) Comparison between Location 2, 3 and 4

aggregate(dBA ~ Location_Name, data=data2, sd)
##                     Location_Name       dBA
## 1       Location 2 (IMSE Hallway) 1.3316656
## 2 Location 3 (Livermore center 1) 0.7767453
## 3 Location 4 (Livermore center 2) 0.8736895

Interpretation:

  • Since p-value << 0.05, there is a statistically significant difference in mean dBA levels across the three locations ( 2, 3 & 4)

  • Hallway has the highest average sound level among 2 ,3 ,4

  • Livermore Center 1 has the lowest noise.

  • Livermore Center 2 falls in between.

  • SD values show the variability of noise levels (dBA) at each location: the IMSE Hallway had the highest fluctuation (SD ≈ 1.33), while the Livermore Centers were more stable with lower variability (SD ≈ 0.77–0.87).

Decision Points

Taking into account both the dBA vs dBC frequency analysis (Location 1) and the ANOVA results (Locations 2–4), the overall findings are:

1.       At high frequency (Lab 240): No significant difference between dBA and dBC.

2.       At low frequency (Lab 240): dBC is significantly higher than dBA; frequency reduction impacts dBA more strongly.

3.       With the reduction of frequency: dBA levels drop significantly more than dBC, indicating that dBA is more sensitive to frequency change.

4.       Across different locations (Hallway vs. Centers): dBA levels vary significantly; hallway is noisier than reading spaces. SD is also higher here .

5.       Quiet spaces (Centers) consistently maintain lower dBA values with lower SD values , suitable for reading/study.

6.       General insight: Both frequency type (high vs low) and environmental location (hallway vs reading center) have strong influence on measured sound levels.

Location wise Lightness (Lux) value Analysis

We measured light intensity (lux) at four locations.
In Lab 240, measurements were taken under Low Light and Maximum Light conditions, allowing for a two-sample t-test.
For Locations 2, 3, and 4, measurements were compared with ANOVA.

Location 1 (Lab 240): Low vs Maximum Light

Observations

  • Low Light (3 obs.): 445 – 446 (mean ≈ 445.3)
  • Maximum Light (3 obs.): 554 – 560 (mean ≈ 557.3)

Statistical Test (Welch’s t-test)

df_lux <- data.frame(
  Location_ID = c(rep(1,6), rep(2,3), rep(3,3), rep(4,3)),
  Location_Name = c(rep("Location 1 (Lab 240)",6),
                    rep("Location 2 (IMSE Hallway)",3),
                    rep("Location 3 (Livermore center 1)",3),
                    rep("Location 4 (Livermore center 2)",3)),
  Lux = c(445,446,445,560,554,558,1200,1183,1197,126,127,126,126,127,127),
  Light_Condition = c(rep("Low Light condition",3),
                      rep("Maximum Light condition",3),
                      rep(NA,9))
)

lab240 <- subset(df_lux, Location_ID==1)
low_light <- lab240[lab240$Light_Condition=="Low Light condition","Lux"]
max_light <- lab240[lab240$Light_Condition=="Maximum Light condition","Lux"]

t_lab240 <- t.test(low_light, max_light, var.equal = FALSE)
t_lab240
## 
##  Welch Two Sample t-test
## 
## data:  low_light and max_light
## t = -62.394, df = 2.1427, p-value = 0.0001568
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -119.251 -104.749
## sample estimates:
## mean of x mean of y 
##  445.3333  557.3333

Interpretation: The p-value < 0.001 indicates a highly significant difference between low and maximum light in Lab 240.

Visualization

boxplot(Lux ~ Light_Condition, data=lab240,
        main="Lab 240: Lux under Low vs Maximum Light",
        ylab="Lux Level", col=c("orange","skyblue"))

Locations 2, 3, and 4

Observations (Lux Ranges)

aggregate(Lux ~ Location_Name, data = subset(df_lux, Location_ID!=1),
          FUN = function(x) c(min=min(x), max=max(x), mean=mean(x)))
##                     Location_Name   Lux.min   Lux.max  Lux.mean
## 1       Location 2 (IMSE Hallway) 1183.0000 1200.0000 1193.3333
## 2 Location 3 (Livermore center 1)  126.0000  127.0000  126.3333
## 3 Location 4 (Livermore center 2)  126.0000  127.0000  126.6667
  • Location 2 (IMSE Hallway): 1183 – 1200 (mean ≈ 1193.3)
  • Location 3 (Livermore Center 1): 126 – 127 (mean ≈ 126.3)
  • Location 4 (Livermore Center 2): 126 – 127 (mean ≈ 126.7)

Statistical Test (ANOVA)

anova_lux <- aov(Lux ~ Location_Name, data=subset(df_lux, Location_ID!=1))
summary(anova_lux)
##               Df  Sum Sq Mean Sq F value   Pr(>F)    
## Location_Name  2 2276267 1138133   41137 3.88e-13 ***
## Residuals      6     166      28                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Visualization

boxplot(Lux ~ Location_Name, data=subset(df_lux, Location_ID!=1),
        main="Lux Comparison Across Locations 2, 3, 4",
        ylab="Lux Level", col="lightgreen")

Standard Deviation (SD) Comparison of Lightness Among Different Location

# SD for Lab 240 by light condition (separate)
aggregate(Lux ~ Location_Name + Light_Condition,
          data = lab240,
          sd)
##          Location_Name         Light_Condition       Lux
## 1 Location 1 (Lab 240)     Low Light condition 0.5773503
## 2 Location 1 (Lab 240) Maximum Light condition 3.0550505
# or (tidyverse)
# df_lux %>% filter(Location_Name=="Location 1 (Lab 240)") %>%
#   group_by(Light_Condition) %>% summarise(SD_Lux = sd(Lux), .groups="drop")


aggregate(Lux ~ Location_Name,
          data = subset(df_lux, Location_Name != "Location 1 (Lab 240)"),
          sd)
##                     Location_Name       Lux
## 1       Location 2 (IMSE Hallway) 9.0737717
## 2 Location 3 (Livermore center 1) 0.5773503
## 3 Location 4 (Livermore center 2) 0.5773503

Final Interpretation & Observation on Lux measurements:

  • In Lab 240, lux increases significantly from low light to maximum light.

  • Across Locations 2, 3, and 4, the hallway has very higher lux, while reading centers remain consistently dim (~126–127).

  • General Insight: Hallways designated tested are much brighter with higher lux value, while reading centers lightness are comparatively dim which is reflecting in their respective Lux value designed to maintain low and stable light levels, suitable for study environments.

  • The ANOVA shows p-value < 0.001, meaning there is a significant difference among locations.
    The hallway is much brighter compared to reading centers, which are consistently around 126–127.

  • From SD comparison The IMSE Hallway showed the highest variability in lighting (SD ≈ 9.07), meaning light levels there fluctuated more across measurements. In contrast, the Livermore Centers and Lab 240 (Low Light) were very stable (SD ≈ 0.58), while Lab 240 (Maximum Light) had moderate variation (SD ≈ 3.06).

Discussion Based on above analysis

Best Location

Livermore Center 1 and 2 showed low variability in noise levels, but their light intensity was very low, which may cause eye strain during prolonged work. According to the Illuminating Engineering Society of North America (IESNA), 300–500 lux is recommended for reading and office tasks, whereas these centers fall far below that standard. The IMSE Hallway exhibited high variability in both noise and light, making it difficult for readers to maintain concentration. In contrast, Lab 240 appears to be the most suitable study environment—particularly under low or no artificial high-frequency sound & maximum light condition , offering quieter conditions and reasonably consistent lighting, which support focused reading and computer-based work.

Strengths & Weaknesses

Lighting Strengths

1. Adequate lux for reading (supported by data ~445–1000 lux) in Lab 240
2. Adjustable artificial lighting (qualitative observation).

Lighting Weaknesses

1. Potential glare under maximum lighting in Hallway
2. Inconsistent natural/artificial light mix.

Sound Strengths

1.       Lower ambient noise than hallways (data support: ~48–91 dBA).

2.       Minimal external disturbances in Livermore & lab area(observation).

3.       Another observed strength of Lab 240 is its low variability (small SD) in both sound and light, ensuring a stable environment for tasks.

Sound Weaknesses

1.       High-frequency equipment noise peaks (data support: 80–91 dBA)

2.        Lack of acoustic treatment (observation).

3.       A weakness in the hallway was the high SD, which indicates inconsistent noise and lighting, making this area less reliable for focused work.


Worst Location

If we disregard the artificially created high-frequency condition in Lab 240, the IMSE Hallway emerges as the least suitable study environment due to its high variability in background noise and fluctuating light levels. Moreover, working for extended periods in an excessively bright area may also lead to eye strain and discomfort.

Suggested Improvements:
Since students often study in the Livermore Centers, which are comparatively quiet but have very low lighting levels, we recommend increasing the illumination to at least 300–500 lux, in line with IESNA standards for reading and office work.

Anticipated Variations

Lighting and noise levels may change with:
- Time of day (morning sunlight, evening artificial light).
- Foot traffic (student rush hours).
- Events (presentations or group activities).

Conclusion

This study highlights the impact of light and sound on workstation quality. Lab 240 emerged as the best study environment, combining acceptable lighting with low background noise. Conversely, IMSE Hallway was least suitable due to high noise fluctuations and more brightness level. Optimizing such environmental factors is crucial for enhancing student productivity and well-being.

Future Work

Future investigations should include:
- Measurements at different times of day and across more locations.
- Professional-grade instruments for greater accuracy.
- Broader analysis of environmental factors (temperature, seating ergonomics).
- Correlating results with subjective user surveys for holistic evaluation.