- Open Access
Influences of wind speed and direction on atmospheric particle concentrations and industrially induced noise
© The Author(s) 2016
- Received: 13 May 2016
- Accepted: 13 October 2016
- Published: 28 October 2016
In this study, the spatial and temporal relationship of wind speed, atmospheric particles concentration, and the industrial-induced noise levels during different times of the day were examined, using sawmill industrial location around Ile-Ife in Osun of Nigeria as a case study.
Mobile devices were used to measure noise level and basic meteorological parameters were examined and their influences on the noise levels distribution were assessed. The maximum and minimum sound levels; Lmax and Lmin, the PM10 and PM1 particle concentrations, wind speeds and directions were measured in the morning (7–9 a.m.), afternoon (12–2) and evening (4–6 p.m.) over 14 consecutive days.
The results revealed that the noise level varies spatiotemporally, much more consistent spatial distribution along the vicinity of sawmill industries. A higher level of noise occurred during the weekday (WD), Leq > 70 dB(A), compared to weekends (WE). Extreme average noise levels are associated with the immediate neighbourhood of sawmill industrial areas during WD compared to streets and road annexes of the study area. The results also show a very weak relationship between noise and PM10 and PMcoarse for both WD and WE with r < 0.35 for PM1 and r < 0.20 for PMcoarse. There appears to be a moderate significant correlation between noise level and PM1 during some meteorological conditions with r > 0.51.
The slight relationship between noise and PM1 is perhaps a result of wind movement that carries particles from the source region since booth noise and particles mostly originate from the sawmill. The study concludes that wind speeds and directions have a significant influence on both noise level and particle concentration within the study sites.
- Wind speed/direction
- Atmospheric particles
- Noise levels
Noise pollution has been one of the environmental hazards as early as the inception of civilization to the recent era of technology. Noise pollution has been associated with human activities and persistent human interaction with the environment. Over the past decades, noise pollution has received increased attention and studies have reported that noise pollution is one of the environmental hazards affecting human, the effects range from annoyance to difficulty in falling asleep, which latter leads to high blood pressure (WHO 2005; Ugwuanyi et al. 2005; Den Boer and Schroten 2007; Tetreault et al. 2013). Studies have stated that noise pollution causes hearing impairment, physiological and mental illness, and in many cases prompts behavioural and social effects (Den Boer and Schroten 2007; Pathak et al. 2008; Weber 2009; Ballesteros et al. 2010; WHO 2011, 2013; Lee 2014). A study by De Vos and Van Beek (2011) has revealed that about two billion people in cities around the world are subject to over 55 dB(A) noise level. But, a report by the European Environment Agency, EEA (2014) estimated that nearly 115 million people in Europe are exposed to average day/night time noise levels of about 55 dB(A). The findings from these studies revealed that noise pollution is severe in urban areas, especially in less developed countries where insufficient control is exercised, mainly if the cities are poorly planned (Pathak et al. 2008; Foraster 2013).
The need to understand environmental noise and its impacts on people in urban areas has driven other researchers in both developed and developing countries. In developed countries, several noise pollution guidelines have been developed. In sub-Saharan Africa, although, where noise legislations exist, they are often poorly enforced and implemented (Sonibare et al. 2004; Chung et al. 2005; Oyedepo and Saadu 2009; Sørensen et al. 2011; Oyedepo 2012). However, interest may be attributed to the current development of guidelines and standards for environmental pollution by the Federal Environmental Protection Agency (FEPA 1991). Unlike cities in developed countries, surprisingly, few studies have been carried out in sub-Saharan Africa which assessed spatial and temporal patterns in noise level and relate this to meteorological parameters. There is a need for examination of atmospheric air pollution and environmental noise, their relationship and how this could affect cities inhabitants. Thus, this study aims at evaluating the spatial and temporal variations in the noise levels during different times of the day and examines influences of the wind on industrial-induced noise pollution in sawmills industrial location around Ile-Ife in Osun of Nigeria. The study focuses on assessing the spatiotemporal relationship between wind speed and direction and noise level and determine the extent noise and particles have a common cause. The basic meteorological parameters were examined and their influences on the distribution of industrial-induced and traffic noise were assessed.
Revisiting the sample locations was guided by Global Positioning System (GPS). Sets of Garmin GPSs with 0.2% resolution accuracy (Fig. 2c) were used in the field to locate sample points and record the coordinates of all sample points for effective mapping. Noise levels for both weekdays (WD) and weekends (WE) were mapped using inverse distance weighting (IDW) interpolation technique as stated by Burrough and Rachael 1998; Ayanlade 2009; Samanta et al. 2012; Pokhrel et al. 2013. This was performing in Quantum GIS environment. IDW was calculated using weights change which was based on the linear distance of the noise sample points from the unsampled points in inverse proportions. The implication of this on noise measurement is that noise level values closer to the un-sampled locations are more representative of the value to be estimated than values from samples farther away. Thus, IDW was used to interpolate deviations from a long-term mean in noise level data over the study periods.
Noise level at different sampling sites
Meteorological and pollutant situation during the measurement periods
Daily averages of different meteorological measures during both weeks
Noise level Leq (dB[A])
95.95 ± 15.51 (112)
96.84 ± 14.48 (120)
PM1 (µg m−3)
32.21 ± 12.82 (53)
28.18 ± 9.73 (45)
PM10 (µg m−3)
49.32 ± 18.31 (73)
33.41 ± 13.01 (58)
Ws (m s−1)
1.55 ± 0.82 (2.42)
2.20 ± 0.55 (3.01)
Wind direction (deg.)
302 ± 36.04 (350)
256 ± 7.15 (268)
29.71 ± 1.41 (39)
27.23 ± 2.86 (33)
Correlation analysis of noise and meteorological situation during the measurement periods
Correlations between noise level and particle concentrations during the two weeks of measurement
Ws vs PM1
Leq vs PM1
Leq vs PMcoarse
Correlations between noise level, number of working sawmilling machines and particle concentrations during measurement (comparing WD and WE)
Leq vs no of working sawmilling machines
Leq vs PM1
Leq vs PMcoarse
This study draws upon two set of data; noise measurement and meteorological data to examine spatial and temporal variation in wind and it influences on the noise level. The samples measurements were conducted during the period from 26 January through 9 February 2015 in sawmill industrial area in Ile-Ife. Using mobile measurements, the study aims at assessing effects of wind speed/direction on industrial-induced noise. The results from this study revealed that: (1) noise levels in the study area are by far exceeding the EPA noise standard for industrial area; (2) noise level demonstrate a very consistent spatial distribution, higher along the vicinity of sawmill industries and this is independent of meteorology conditions; (3) there are slight correlation relationship between noise and PM1 due to weak turbulent mixing, during week 1, resulted from limited dispersion of particle during measurement; (4) extreme average noise levels are associated with the immediate neighbourhood of sawmill industrial areas during WD compared to streets and road annexes of the study area. However, noise levels were more along the highway roadside during WE when the majority of the sawmill industries were not in operation.
The results from this study revealed that, in many cases, particle sizes do not have a similar response to meteorological conditions and that wind speeds and directions have significantly influence on both noise level and particle concentration (Jamriska and Morawska 2008; Nicolas et al. 2009; Can et al. 2011). These results imply that particle concentrations and distributions are strongly varied with wind speed and direction as they move away from the major source (Zhang et al. 2004; Muzet 2007; Owoade et al. 2013). The influences of meteorological conditions on noise levels appears much more complex. The results show that the correlation between noise level and PM1 appears relatively moderate during weak turbulent metrological conditions. Previous studies have earlier reported a moderate correlation between noise level and atmospheric particles. The foremost studies include Weber and Litschke (2008) and Davies et al. (2009) which established a slight relationship between some particle concentrations and noise levels. The major finding of the present study is that noise levels and atmospheric particles sensitive to wind speeds and directions as they cover distances from the sources. It is also obvious from this study that sawmill mechanical noise is the major sources of the noise in the study area, noise that results from all kinds of sawmill operation and power capacity engines. Generally, noise levels demonstrate very consistent spatial distributions along locality of sawmill industries and this is independent of meteorology conditions. But, weak turbulent mixing results to moderate association between noise level and some particle concentrations. The spatial correlation between particle concentrations and the noise level was largely weak as a result of higher turbulent mixing and some changes in the direction of the ambient wind during measurement periods.
AA played the leading role in the conception and every aspect of this study. EFO contributed in data collection, data analysis, and other various levels of expertise. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Adejuwon JO, Jeje LK (1975) Land element of the environmental system of ife area. In: Ojo A (ed) Environment resource base project, no 2. Department of Geography, University of Ife, IfeGoogle Scholar
- Ayanlade A (2009) Seasonal rainfall variability in Guinea Savanna part of Nigeria: a GIS approach. Int J Clim Change Strateg Manag 1(3):282–296View ArticleGoogle Scholar
- Ballesteros MJ, Fernandez MD, Quintana S, Ballesteros JA, Gonzalez I (2010) Noise emission evolution on construction sites. Measurement for controlling and assessing its impact on the people and on the environment. Build Environ 45(3):711–717View ArticleGoogle Scholar
- Burrough PA, Rachael AM (1998) Principal of geographical information systems. Oxford University Press, OxfordGoogle Scholar
- Can A, Rademaker M, Van Renterghem T, Mishra V, Van Poppel M, Touhafi A, De Baets B, Botteldooren D, Theunis J (2011) Correlation analysis of noise and ultrafine particle counts in a street canyon. Sci Total Environ 409(2011):564–572View ArticlePubMedGoogle Scholar
- Chung JH, Des Roches CM, Meunier J et al (2005) Evaluation of noise-induced hearing loss in young people using a web-based survey technique. Pediatrics 115:861–867View ArticlePubMedGoogle Scholar
- Davies HW, Vlaanderen JJ, Henderson SB, Brauer M (2009) Correlation between coexposures to noise and air pollution from traffic sources. Occup Environ Med 66:347–350View ArticlePubMedGoogle Scholar
- De Vos P, Van Beek A (2011) Environmental noise. Encycl Environ Health 1:476–488View ArticleGoogle Scholar
- Den Boer LC, Schroten A (2007) Traffic noise reduction in Europe. Health effects, social costs and technical and policy options to reduce road and rail traffic noise. Report, CE Delft. http://www.ce.nl. Accessed 15 Mar 2014
- EEA (2014) Noise in Europe 2014. Publications Office of the Europea Union, LuxembourgGoogle Scholar
- FEPA (1991) National interim guidelines and standards for industrial effluent, gaseous emissions and hazardous waste in Nigeria. Federal Environmental Protection Agency Decree. http://www.placng.org/new/laws/F10.pdf. Accessed 15 Mar 2014
- Foraster M (2013) Is it traffic-related air pollution or road traffic noise, or both? Key questions not yet settled! Int J Public Health 58(5):647–648View ArticlePubMedGoogle Scholar
- Jamriska M, Morawska LK (2008) The effect of temperature and humidity on size segregated traffic exhaust particle emissions. Atmos Environ 42:2369–2382ADSView ArticleGoogle Scholar
- Kaur S, Nieuwenhuijsen MJ, Colvile RN (2007) Fine particulate matter and carbon monoxide exposure concentrations in urban street transport microenvironments. Atmos Environ 41:4781–4810ADSView ArticleGoogle Scholar
- Lee HY (2014) Long-term evolution of campus noise emissions: a case of new university development. J Environ Plan Manag 57(8):1169–1182View ArticleGoogle Scholar
- Muzet A (2007) Environmental noise, sleep and health. Sleep Med Rev 11:135–142View ArticlePubMedGoogle Scholar
- Nicolas JF, Yubero E, Pastor C, Crespo J, Carratalá A (2009) Influence of meteorological variability upon aerosol mass size distribution. Atmos Res 94:330–337View ArticleGoogle Scholar
- Ojo O (1977) The climates of West Africa. Heinemann Educational Books Ltd, OxfordGoogle Scholar
- Owoade OK, Jegede OO, Ayoola MA, Fawole OG, Bashiru MI, Olise FS, Ogundele LT (2013) Concentrations of particulate matter from an iron and steel smelting plant located along a busy high way in southwestern Nigeria. Ife J Sci 15(1):31–39Google Scholar
- Oyedepo SO (2012) Noise pollution in urban areas: the neglected dimension. Environ Res J 6(4):259–271View ArticleGoogle Scholar
- Oyedepo OS, Saadu AA (2009) A Comparative study of noise pollution levels in some selected areas in Ilorin metropolis, Nigeria. Environ Monit Assess 158:155–167View ArticlePubMedGoogle Scholar
- Pathak V, Tripathi BD, Mishra VK (2008) Evaluation of traffic noise pollution and attitudes of exposed individuals in working place. Atmos Environ 42:3892–3898ADSView ArticleGoogle Scholar
- Pokhrel RM, Jiro K, Shinya T (2013) A kriging method of interpolation used to map liquefaction potential over alluvial ground. Eng Geol 152(1):26–37View ArticleGoogle Scholar
- Samanta DK, Pal D, Lohar B (2012) Interpolation of climate variables and temperature modelling. Theor Appl Climatol 107:35–45ADSView ArticleGoogle Scholar
- Sonibare JA, Akeredolu FA, Latinwo I, Solomon BO (2004) Impact of tanneries on ambient noise levels in Kano, Nigeria. Afr J Environ Assess Manag 8:1–18Google Scholar
- Sørensen M, Hvidberg M, Andersen ZJ, Nordsborg RB, Lillelund KG, Jakobsen J, Tjønneland A, Overvad K, Raaschou-Nielsen O (2011) Road traffic noise and stroke: a prospective cohort study. Eur Heart J 32(6):737–744View ArticlePubMedGoogle Scholar
- Tang UW, Wang ZS (2006) Determining gaseous emission factors and drivers’s particle exposures during traffic congestion by vehicle following measurement techniques. J Air Waste Manag Assoc 56:1532–1539View ArticlePubMedGoogle Scholar
- Tang UW, Wang ZS (2007) Influences of urban forms on traffic-induced noise and air pollution: results from a modelling system. Environ Model Softw 22:1750–1764View ArticleGoogle Scholar
- Tetreault LF, Perron S, Smargiassi A (2013) Cardiovascular health, traffic-related air pollution and noise: are associations mutually confounded? A systematic review. Int J Public Health 58(5):649–666View ArticlePubMedPubMed CentralGoogle Scholar
- Ugwuanyi J, Ahemen UI, Agbendeh AA (2005) Assessment of environmental noise pollution in Makurdi metropolis, Nigeria. Zuma J Rure Appl Sci 6:134–138Google Scholar
- Weber S (2009) Spatio-temporal covariation of urban particle number concentration and ambient noise. Atmos Environ 43:5518–5525ADSView ArticleGoogle Scholar
- Weber S, Litschke T (2008) Variation of particle concentrations and environmental noise on the urban neighbourhood scale. Atmos Environ 42:7179–7183ADSView ArticleGoogle Scholar
- WHO (2005) United Nations road safety collaborations. A handbook of partner profiles. World Health Organisation, GenevaGoogle Scholar
- WHO (2011) Burden of disease from environmental noise—quantification of healthy life years lost in Europe. WHO Regional Office for Europe, BonnGoogle Scholar
- WHO (2013) Review of evidence on health aspects of air pollution—REVIHAAP project. Technical report. http://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAPFinal-technical-report-final-version.pdf. Accessed 19 Mar 2014
- Zhang KM, Wexler AS, Zhud YF, Hinds WC, Sioutas C (2004) Evolution of particle number distribution near roadways. Part II: the ‘road-to-ambient’ process. Atmos Environ 38:6655–6665ADSView ArticleGoogle Scholar