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Spatial-Temporal Differentials in Traffic and Music Generated Noise at Selected Sites in Kisumu City, Kenya

Article Information

Walter Ogutu Amulla1*, Aaron Gichaba Misati2

1Department of Public Health. University of Eastern Africa, Baraton, Kenya

2Department of Public Health. Kabarak University, Kenya

*Corresponding Author: Walter Ogutu Amulla, Department of Public Health. University of Eastern Africa, Baraton, Kenya

Received: 16 March 2021; Accepted: 22 March 2021; Published: 02 April 2021

Citation: Walter Ogutu Amulla, Aaron Gichaba Misati. Spatial-Temporal Differentials in Traffic and Music Generated Noise at Selected Sites in Kisumu City, Kenya. Journal of Environmental Science and Public Health 5 (2021): 169-179.

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Abstract

Despite its well-documented effects on health and wellbeing, noise remains one of the most poorly regulated type of pollution in African cities. Some studies have shown that automobiles and music stores are among the leading sources of noise pollution in African urban centers with equivalent sound pressure levels largely exceeding regulatory limits. These elevated noise levels exposes the public to auditory and non-auditory effects that impair health and quality of life. Regardless, research on road traffic and music-generated noise remain scarce in Kenya and Africa at large. This study sought to assess noise associated with traffic and music at 50 purposively selected sites in Kisumu city, Kenya. Sound Pressure Levels (SPL) were measured using EXTECH® digital sound level meter, recorded in a data sheet and analyzed descriptively on SPSS version 23. Results showed that the mean traffic-generated noise was 70.39 ± 10.10dBA, while music-generated noise was 86.35 ± 6.92dBA. Independent sample t-test showed that the mean SPL for music was significantly higher than traffic. There was considerable variability in traffic noise by site with highways having highest (76.25 ± 5.42dBA) followed by roundabouts (75.0 ± 4.97 dBA) and lastly by termini (71.60 ± 4.81dBA). Noise at resting parks varied with distance from high traffic zones. Both vehicular and music-related noise exceeded maximum permissible limits, but music-related noise was significantly higher than vehicular noise.

Keywords

Traffic; Noise; Music; Kisumu; Spatial

Traffic articles; Noise articles; Music articles; Kisumu articles; Spatial articles

Article Details

1. Introduction

Developing countries have been experiencing exponential growth in urbanization. Incidentally characteristic of industrialized countries, the phenomenon has shifted substantially to low and middle income countries [1]. This growth is not without negative consequences. In Africa, it is posited that rapid urbanization has become a threat to sustainable development due in part to rising pollution and unregulated informal economy [2].

Despite its well-documented effects on health, noise remains one of the most unregulated and disregarded types of pollution especially in developing countries [3]. The phenomenon is particularly of concern in urban Africa due to social and traffic-related noise compounded by poor noise regulation [4, 5].

Automobiles and music stores are among the leading sources of noise pollution in African urban centers. A study by Ebare et al (2011) [6] in a Nigerian city found that more than 90% of music stores generated noise levels exceeding 85 dB while Oyedepo and Saadu (2009) [7] reported that busy roads were the second-highest polluted acoustic environment after industrial areas, with sound pressure levels exceeding 90 dB(A). Elsewhere, Zaki (2012) [8] reported that ambient noise levels were highest in high-traffic density streets of Egypt.

These elevated noise levels have been associated with increased cardiovascular morbidity and mortality in the general population [9], increased adiposity and other markers of obesity [10, 11], risk of mortality from type 2 diabetes (12), respiratory mortality [12, 13] hearing impairment [14], hypertension [15] and other auditory and non-auditory effects [16, 17].

Exposure generally varies with time and place [18, 19] with roads and commercial areas being among the worst polluted and significant differences observed between diurnal and nocturnal sound pressure levels [20, 21]. Other factors affecting variability include prevailing meteorological conditions, road surface texture, gradient and circumambient topography; vehicle types, ages, speed, volume, break type, horns sound pressure, goods transported and driver behavior [15, 22].

Despite its importance to public health research on road traffic and commercial noise are rare in Africa and particularly Kenya. A literature search on PubMed (with search phrases road traffic noise Africa and road traffic noise Kenya) returned a few publications for Africa [7, 8, 23, 24] but none for Kenya. The alternative, but rather general search phrase, “urban noise Africa”, returned slightly more publications but only six were relevant and most of these representing only one country-Nigeria [4-6, 25-27].

Narrowing the search phrase to traffic noise Kenya revealed a few more hits [3, 28]. Wawa and Mulaku (2015) [3] mapped environmental noise in Nairobi and listed road traffic among the leading noise sources but did not report corresponding noise levels while Goshu and colleagues (2017) [28] evaluated extent of in-vehicle music-related noise pollution in Nairobi and reported all exceeded local regulatory limits.

Surprisingly, no studies were found for Kisumu City, despite it being the third-largest in Kenya with considerably high traffic. Moreover, anecdotal evidence suggested high noise emanating from music stores and churches within the city. Against the backdrop of public health concern and paucity of literature, this study assessed geospatial distribution of traffic and music-related noise from selected sites in the Kenyan city of Kisumu.

2. Materials and Methods

2.1 Study area and design

Adopting descriptive cross-sectional design, the study was conducted in Kisumu city, the third-largest city in Kenya after Nairobi and Mombasa. Noise assessments were done at 50 purposively selected high traffic areas and noise-prone music centers within the city’s CBD. Study locations were tagged with GPS coordinates using an android GPS application (Mobile Topographer, version 9.0.0) and the predominant noise source along with prevailing geospatial features in the area noted for further characterization of the sites.

2.2 Data collection and analysis

Noise assessments were taken as close to source as practicable without attracting undue attention using a factory calibrated EXTECH® digital sound level meter (model 407732). For traffic noise, 20 roadside readings of continuous A-weighted equivalent sound pressure levels (LAeq) were recorded per site. The readings were captured as vehicles passed or in the case where traffic was continuous at intervals of 15-20 seconds.

For music-related noise 20 continuous A-weighted equivalent sound pressure level (LAeq) readings were recorded at intervals of 15-20 seconds per site. Music-related noise were measured at music stores, promotional campaigns, inside public service vehicles and churches. Noise data was collected at mid-morning, afternoon and evening hours as these timelines corresponded with traffic volumes.

The data was analyzed on SPSS version 23. Arithmetic means with standard deviations and 95% confidence intervals were calculated and compared (Single-sample t-test, Independent sample t-test and One-way ANOVA with Games-Howell post hoc) across categories of sources and data presented on graphs and tables.

3. Results

3.1 Levels of traffic and music-related noise

Source

n

Mean (dBA)

STD

Min

Max

Traffic

Highway

220

76.2500

5.42666

66.00

104.00

Parks

120

54.0667

5.13308

44.00

66.00

Roundabout

120

75.0000

4.97895

66.00

94.00

Terminus

100

71.6000

4.81580

65.00

87.00

Overall

560

70.3982

10.10289

44.00

104.00

Music

Music stores

380

85.8211

6.73898

73.00

107.00

Church

20

98.500

.82717

97.00

100.00

In-vehicle

40

85.400

4.3133

77.00

94.00

Overall

440

86.3591

6.92381

73.00

107.00

Table 1: Levels of noise associated with traffic and music in Kisumu City.

As indicated in table 1 the mean (arithmetic) traffic-related noise was (70.3982 dBA). Further, the results of one-sample t-test indicated that mean (arithmetic) traffic noise was significantly higher than NEMA’s stipulated (60dBA) value for commercial zones (t=24.35, df=559, p=0.000).

Music sources generated very high levels of noise and in many cases exceeded 100 dB(A). The most notorious being music stores (107dB LAmax) and church music (100dB LAmax). The Mean Music-related noise (86.3591dBA) was also significantly higher than NEMA’s threshold (t= 79.857, df= 439, p=0.000) as per the results of single-sample t-test.

3.2 Spatial differentials in noise levels

fortune-biomass-feedstock

Figure 1: Means plot of noise levels across sites.

As illustrated (Figure 1) resting parks recorded the lowest noise levels, while the highest noise was associated with religious music. However, levels of noise at resting parks (Figure 2) also varied with proximity to CBD with the closest to the CBD (Oile) recording the highest mean SPL (61.20 ± 2.19089, CI: 60.175 - 62.225) and the farthest (Victoria) recording the lowest (46.75 ± 1.37, CI: 46.108 - 47.392). One-way ANOVA with multiple pairwise comparisons (table 2) revealed that the means differed significantly across all parks except Barclays-Aga Khan pair (F = 219.781, p = 0.000).

fortune-biomass-feedstock

Figure 2: Variation in resting park noise with distance from high traffic zones.

Likewise, there was considerable variability in traffic noise by site, with highways having highest (76.25 ± 5.42dBA) followed by roundabouts (75.0 ± 4.97 dBA) and lastly by termini (71.60 ± 4.81dBA). One-way ANOVA with multiple pairwise comparisons revealed that these means differed significantly for highways vs. termini (p = 0.000), roundabouts vs. termini (p = 0.000) but not for highways vs. roundabouts (p = 0.056).

Further, independent sample t-test showed that the mean SPL for music-related sources was significantly higher than traffic (t= 29.577, df= 980.790, p=0.000, 95%CI: 14.90188-17.01987). Music was thus a leading source of environmental noise in the city.

Multiple Comparisons

Dependent Variable: LAeq

Games-Howell

(I) Site_coded

(J) Site_coded

Mean Difference

(I-J)

Std. Error

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

Oile

Sportsground

3.90000*

.52666

.000

2.2758

5.5242

AgaKhan

6.75000*

.76459

.000

4.4525

9.0475

Barclays

5.60000*

.51299

.000

4.0060

7.1940

Victoria

14.45000*

.57800

.000

12.6989

16.2011

Jamhuri

12.10000*

.52566

.000

10.4781

13.7219

Sportsground

Oile

-3.90000*

.52666

.000

-5.5242

-2.2758

AgaKhan

2.85000*

.61804

.002

.9327

4.7673

Barclays

1.70000*

.24602

.000

.9598

2.4402

Victoria

10.55000*

.36256

.000

9.4518

11.6482

Jamhuri

8.20000*

.27145

.000

7.3857

9.0143

AgaKhan

Oile

-6.75000*

.76459

.000

-9.0475

-4.4525

Sportsground

-2.85000*

.61804

.002

-4.7673

-.9327

Barclays

-1.15000

.60643

.431

-3.0427

.7427

Victoria

7.70000*

.66233

.000

5.6792

9.7208

Jamhuri

5.35000*

.61719

.000

3.4346

7.2654

Barclays

Oile

-5.60000*

.51299

.000

-7.1940

-4.0060

Sportsground

-1.70000*

.24602

.000

-2.4402

-.9598

AgaKhan

1.15000

.60643

.431

-.7427

3.0427

Victoria

8.85000*

.34240

.000

7.8032

9.8968

Jamhuri

6.50000*

.24387

.000

5.7665

7.2335

Victoria

Oile

-14.45000*

.57800

.000

-16.2011

-12.6989

Sportsground

-10.55000*

.36256

.000

-11.6482

-9.4518

AgaKhan

-7.70000*

.66233

.000

-9.7208

-5.6792

Barclays

-8.85000*

.34240

.000

-9.8968

-7.8032

Jamhuri

-2.35000*

.36110

.000

-3.4443

-1.2557

Jamhuri

Oile

-12.10000*

.52566

.000

-13.7219

-10.4781

Sportsground

-8.20000*

.27145

.000

-9.0143

-7.3857

AgaKhan

-5.35000*

.61719

.000

-7.2654

-3.4346

Barclays

-6.50000*

.24387

.000

-7.2335

-5.7665

Victoria

2.35000*

.36110

.000

1.2557

3.4443

*. The mean difference is significant at the 0.05 level.

Table 2: Levels of noise across resting parks (multiple comparisons).

4. Discussion

Despite its well-documented effects on health and wellbeing, noise remains one of the most poorly regulated type of pollution in African cities. This study assessed noise associated with traffic and music at 50 purposively selected sites in Kisumu city, Kenya.

Traffic-related noise exceeded the national environmental management authority’s (NEMA) limit by over 10dBA but was considerably lower than those recorded in Nairobi (90-98 dBA) [29]. This might be due to heavier traffic density in Nairobi but could also be offset by the fact that the authors calculated logarithmic mean rather than arithmetic mean which often gives higher values. However, logarithmic means are appropriate only when measurements are continuous.

Traffic noise levels exceeding 70 dBA were associated with annoyance and work inefficiency in an Indian city [30], as well as a number of other health effects around the world [31, 32]. The levels of traffic noise reported in this study thus indicate the exposed populations were at risk of these effects.

Music-related noise exceeded state regulatory limits by over 26dBA with some sources emitting noise levels in excess of 107dBA. Similar noise levels were reported by Ebare et al., (2011) [6] in a Nigerian urban center. These elevated noise levels are risk factors to noise-induced hearing loss (NIHL), irritation and hypertension among other auditory and non-auditory effects [14, 15].

Noise levels varied significantly with place and source. Highest traffic-related noise were recorded at highways and parks close to busy roads. Vehicular noise is a function of many variables including vehicle type and speed [15, 22]. In this study, however, the variation in noise was unlikely caused by speeding as there was no significant difference between mean SPLs recorded at highways and roundabouts. Traffic density and vehicle types might explain the differentials.

While noise data in this study was cross-sectional, the measurements are useful proxies for chronic and/or acute exposure of the local public [25] especially in places with little or no noise surveillance. The findings of this study should thus interventions as well as further investigation. Methods to mitigate noise in urban centers have been proposed in previous studies. Ow and Ghosh (2017) [33] reported that cultivating trees along roads can reduce vehicular noise by up to 11 decibels. Others proposed low-noise tyres and pavements, speed regulations, traffic flow reduction and use of sonic crystals as barriers [34, 35]. City and national environmental authorities could explore these options for mitigation.

5. Conclusion

As Kenya strives towards universal health coverage, environmental noise should not be neglected. This study assessed levels and distribution of traffic and music-related noise in Kisumu city and evaluated noise against regulatory limits. Overall, both vehicular and music-related noise exceeded maximum permissible limits, but music-related noise was significantly higher than vehicular noise. The key contribution of this research to the public is the discovery of spatial differentials in environmental noise at resting parks in the city which should inform the public’s choice of green space.

Recommendations

NEMA should step up enforcement of music-related noise regulations since this survey suggests gross violation. The county’s department of health should take measures to protect the habitually exposed including improving vegetation along high traffic roads. Alternatively, the county government should provide additional resting parks outside high traffic zones. The public should use green spaces further from high traffic zones or considerably barricaded from traffic and music-related noise.

Further Research

Audiometric assessment for at-risk population especially those running music stores. More robust noise surveillance over longer durations, with continuous rather than intermittent, measurement should be conducted.

Limitations

This was a cross-sectional survey of noise levels within a limited period of time thus may not be taken as gold-standard since noise fluctuates widely across time.

Conflict of Interest

The authors declare no conflict of interest.

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