ABSTRACT
The present study investigated the environmental impacts of cement production on surface water quality and vegetation in the vicinity of the industry. A 2-year study was conducted, covering both wet and dry seasons. Water samples were collected from the Akinbo River, where the cement industry discharges its liquid waste and effluents, and analysed for physical and chemical parameters using standard procedures. The vegetation around the factory was analysed for chlorophyll, plant density, basal areas, and heights of woody species. The study also assessed the health impacts of cement production on the factory employees and the residents living in the vicinity of the factory. The health assessment was based on hospital information obtained from two clinics in the villages near the factory. The water quality results revealed elevated concentrations of Ca and Fe. All the trees sampled around the study area had small basal areas and short heights. There was also a significant reduction in the chlorophyll contents of the vegetation. The health study showed a high incidence of upper-respiratory tract infections, cardiovascular diseases, arthritis, and dermatitis among factory workers. This study suggests a review of the existing dust-suppressing system put in place by the industry, to protect public health.
HIGHLIGHTS
The environmental impacts of cement production on surface water quality were investigated.
Water samples were collected and analysed for physical and chemical parameters.
The water quality results revealed elevated concentrations of Ca and Fe.
A high incidence of upper-respiratory tract infections, cardiovascular diseases, arthritis, and dermatitis was observed.
INTRODUCTION
Cement production in Nigeria has grown from about 8 million metric tonnes in the decade to about 58 million tonnes per year (allAfrica.com 2008; Ademola & Oluseyi 2013; Etim et al. 2021). Cement companies in Nigeria are either independently owned and operated locally as joint venture companies or state-operated. Cement production is a dusty operation connected with many processes, including the crushing and extraction of mined rocks, milling activities, and kiln operations (Abdul Wahab et al. 2021). It also involves water-intensive processes like quarrying, dust suppression, and cooling of machinery. Contaminants such as heavy and trace elements and organic compounds like dioxins and furans are released via cement kilns (Van Oss & Padovani 2002; Arfala et al. 2018). Runoff from cement plants can carry these pollutants into nearby surface water bodies, potentially contaminating them and impacting aquatic life and water quality. Liquid waste and effluent generated by the quarry, press house, and milling operations during cement production may also wash into the surface water, thereby impairing it.
Cement production releases particulate matter and gaseous pollutants (NOx, SO2, and CO2) into the environment (Huntzinger & Eatmon 2009; Lei et al. 2011; Voicu et al. 2020). These air pollutants are carried by the local prevailing winds to the neighbouring villages, where they could trigger various health problems. Workers in cement plants are exposed to different occupational hazards, including dust containing silica, cement kiln dust, and chemical emissions. Prolonged exposure can result in respiratory diseases such as silicosis, skin irritations, and other health issues if adequate safety measures and personal protective equipment (PPE) are not implemented and maintained (Al-Neaimi et al. 2001; Meo et al. 2002; Merenu et al. 2007). The noise generated from the quarry, cement, and raw mills may have adverse effects on human health.
Quarrying activities associated with cement production can lead to habitat loss and fragmentation, affecting local vegetation. Dust emissions and pollutants from the cement plant can settle on plants, potentially impairing their health and contributing to biodiversity loss in the surrounding areas. Many studies have been conducted at the Ewekoro cement factory when the system of production was operating on a wet system of cement manufacture (Asubiojo et al. 1991; Adejumo et al. 1994; Salami et al. 2002; Olaleye 2005). However, few studies were reported on the dry system of cement production at the Ewekoro factory (Inegbenebor et al. 2018), especially on surface water quality, vegetation, and human health. The present study evaluated the impacts of the dry system of cement manufacture on surface water quality, vegetation, and human health.
MATERIALS AND METHODS
Study area
Map of Ogun State of Nigeria showing the study and control sites located with Ewekoro Local Government Area of the State.
Map of Ogun State of Nigeria showing the study and control sites located with Ewekoro Local Government Area of the State.
The soil in Ewekoro is influenced by the underlying geology, which is mainly composed of 11–12 m of limestone. It is sandy at the base, grading downward into the Abeokuta Formation. The soils are ferratic tropical soils that are old, deep, highly weathered, and red (Areola 1982). The Ewekoro Formation is overlain by phosphatic glauconitic grey shale (Jones & Hockey 1964). The limestone is classified (based on microfacies) into biomicrosparite, shelly biomicrites, algal biosparite, and phosphatic biomicrites in that stratigraphic order (Fidelis Ushie & Affiah 2014). Soils in the vicinity of limestone quarries and cement plants may have varying characteristics due to mining and industrial activities.
The vegetation in Ewekoro and its surroundings can vary, but it often includes typical West African savanna vegetation with scattered trees and grasslands. However, the extensive quarrying and industrial activities may have impacted local vegetation patterns and biodiversity. The vegetation is subjected to annual burning.
The cement industry produces about 10.5 million metric tonnes of cement per year (Isaiah et al. 2021). The technologies of production in the Ewekoro factory began with a semi-wet system in 1960, a wet in 1978, and a purely dry system since 2002. It is a modern, dry process plant where the raw materials are crushed and fed into a pre-heater tower before the hot mill (at 880 °C and 90% calcined) enters a rotary kiln (NIRAS-LTS, E4tech, AIGUASOL & Aston University 2021).
Ewekoro's hydrology is influenced by the local rivers and streams, which may be affected by quarrying activities and industrial water usage. The groundwater system could also be impacted by limestone mining and cement production processes.
Sample collection and analysis
Surface water collection and analysis
Water samples were collected along the Akinbo River, the only river into which the cement factory discharges its wastewater, at intervals of 500 m. The Akinbo River originates from the Ewekoro River and flows into the Alagutan River approximately 10 km from the factory. Over a 2-year period from 2005 to 2006, encompassing both dry (November–March) and rainy (April–October) seasons, the mean annual rainfall ranged between 1,300 and 1,600 mm with a temperature variation of 24–28 °C. Water samples were collected at five sites. A total of 72 surface water samples were collected during the study period. Each sample underwent analysis for temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), total suspended solids (TSS), hardness, dissolved oxygen (DO), and metals (potassium (K), Sodium (Na), calcium (Ca), magnesium (Mg), chromium (Cr), copper (Cu), lead (Pb), manganese (Mn), iron (Fe), and zinc (Zn)). Control water samples were obtained from a river in Obada Oko, situated about 22 km from the factory.
Water parameters were determined using the standard methods outlined in APHA (2005). Temperature, DO, pH, and conductivity were measured on site for accuracy. Temperature, pH, TDS, and conductivity were assessed using a battery-operated electronic Hanna multipurpose meter, which was calibrated with standard buffer solutions (4.0 and 9.0) before each sampling. A 10 mL water sample was measured, and the combined temperature/pH/TDS probe was inserted to take the reading. DO was also determined electronically with a Jenway 9071 battery-operated DO meter.
TSS were determined by the gravimetric method (APHA 2005) through filtration of 50 mL of water samples into a preweighed beaker. The beaker was oven-dried at 105 °C and the final weight taken. Total hardness (TH) was determined via the titrimetric method using an ethylenediaminetetracetic acid (EDTA) solution. The procedure involved measuring a 25 mL water sample into a conical flask and the introduction of 1 mL of buffer solution, followed by the addition of 3–4 drops of Eriochrome Black T (EBT) indicator. The resulting solution was titrated against a 0.05 M EDTA solution until the colour changed from deep purple to blue colour.
For metal determination (APHA 2005), water samples were digested with concentrated nitric acid (10 mL acid + 100 mL water sample) on a hot plate for 30 min. The digested samples were then filtered and sent to the University of Ibadan Laboratory for analysis for metals (Na, K, Ca, Mg, Zn, Cr, Pb, Fe, Cu, and Mn). Na and K levels were analysed using a flame photometer (Jenway PFP7), while Ca, Mg, Zn, Cr, Pb, Fe, Cu, and Mn were determined using an atomic absorption spectrophotometer (AAS; model 210 VGP).
The quality control measures taken during this study include using reagents of analytical grade (Sigma-Aldrich Chemie, GmbH, Germany). Running of blank samples was carried out to cancel matrix effects of the background of the extracting reagents and to calculate the detection limit of the analytical instrument (Taiwo et al. 2020). Metal values below detection limit were replaced with half of detection limit (Croghan & Egeghy 2003). The limit of detection of the instrument varied from 0.005 to 0.10 mg L−1 (Ni), while the limit of quantification varied from 0.05 to 1.0 mg L−1. Details of the AAS instrument are presented in Supplementary Table S1.
Sampling and analysis of vegetation
Plants (Cassava, Manihot esculenta) and sugarcane (Saccharum officinarum) around the vicinity of the industry were collected and analysed for chlorophyll, plant density, basal areas, and heights of woody species. Plant density and leaf abundance were obtained from field enumeration and counting. Five stands of each plant were randomly picked, and all the leaves were counted. Cassava (dicot plant) and sugarcane (monocot plant) were selected for chlorophyll analysis.
Tree heights were measured using the Haga Altimeter.
Health data collection
Medical records of the prevalent diseases and ailments among the cement factory workers, the neighbouring inhabitants, and the residents of Obada Oko (the control area) were collected between 2003 and 2006 from the factory clinic, the State General Hospital located at Itori, and the Medical Center in Obada Oko. Common ailments recorded included upper-respiratory tract (URT) infections, gastrointestinal disorders, cardiovascular diseases (CVDs), dermatitis, carbuncles, conjunctivitis, malaria, measles, insomnia, cuts, colds, general body malaise, abdominal pains, road traffic accidents, loss of appetite, bites of different types, malnutrition, and many others. All these were eventually sifted and reduced to ailments whose causes could be related to dust. The divergence and peculiarity of these diseases vis-à-vis the activities of the inhabitants in the two locations were discussed.
Data analysis
Data collected were analysed for descriptive (Mean ± standard deviation) and inferential () statistics using SPSS for Windows (version 23.0).
RESULTS AND DISCUSSION
Water quality parameters
The results of the water parameters of the Akinbo River for the 2-year sampling period covering both dry and wet seasons are depicted in Tables 1 and 2. The temperature variations observed at various distances from the factory can be attributed to the prevailing climatic conditions (Table 1). The mean temperatures for the wet season (26.02–27.02 °C) and the dry season (26.58–29.77 °C) were still within the typical range for a tropical climate.
Seasonal variations of physico-chemical parameters of the river (N = 72)
Sampling locations . | Temperature (°C) . | pH . | DO (mg L−1) . | EC (μS cm−1) . | TDS (mg L−1) . | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | |
500 m Upstream | 26.6 ± 1.37cd | 26.0 ± 0.88d | 26.3 | 1.61 | 6.43 ± 0.33c | 6.87 ± 0.14b | 6.65 | 4.68 | 6.56 ± 0.89a | 6.70 ± 0.80a | 6.63 | 1.49 | 134 ± 59b | 111 ± 23b | 123 | 13.3 | 118 ± 28a | 112 ± 15a | 115 | 3.69 |
Discharge point | 27.8 ± 0.79abc | 26.6 ± 1.12cd | 27.2 | 3.12 | 7.41 ± 0.30d | 7.35 ± 0.24a | 7.38 | 0.57 | 6.56 ± 0.54a | 6.16 ± 1.42a | 6.36 | 4.45 | 382 ± 88a | 397 ± 45a | 390 | 2.72 | 105 ± 15b | 101 ± 17b | 103 | 2.75 |
500 m Downstream | 28.6 ± 0.81ab | 26.6 ± 1.03cd | 27.6 | 5.12 | 7.21 ± 0.20a | 7.32 ± 0.20a | 7.27 | 1.07 | 6.99 ± 0.98a | 6.45 ± 1.21a | 6.72 | 5.68 | 393 ± 94a | 361 ± 69a | 377 | 6.00 | 109 ± 12b | 102 ± 19b | 106 | 4.69 |
1,000 m Downstream | 29.2 ± 1.57a | 26.9 ± 1.57bcd | 28.1 | 5.80 | 7.29 ± 0.30a | 7.35 ± 0.19a | 7.32 | 0.58 | 6.94 ± 0.59a | 6.43 ± 1.50a | 6.69 | 5.39 | 390 ± 86a | 353 ± 59a | 372 | 7.04 | 111 ± 11b | 107 ± 24b | 109 | 2.59 |
2,000 m Downstream | 29.8 ± 2.21a | 27.0 ± 1.26bcd | 28.4 | 6.97 | 7.41 ± 0.27a | 7.41 ± 0.24a | 7.41 | 0.00 | 7.00 ± 0.54a | 6.41 ± 1.23a | 6.71 | 6.22 | 394 ± 85a | 374 ± 78a | 384 | 3.68 | 110 ± 12b | 103 ± 14b | 107 | 4.65 |
Control (22 km away) | 27.1 ± 1.74bcd | 26.1 ± 0.66d | 26.6 | 2.66 | 6.35 ± 0.40c | 6.77 ± 0.25c | 6.56 | 4.53 | 6.13 ± 0.62a | 6.59 ± 0.98a | 6.36 | 5.11 | 40.8 ± 30c | 40.1 ± 10c | 40.5 | 1.22 | 123 ± 15a | 118 ± 15a | 121 | 2.93 |
Sampling locations . | TSS (mg L−1) . | TS (mg L−1) . | TH (mg L−1) . | . | . | . | . | . | . | . | . | |||||||||
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | . | . | . | . | . | . | . | . | |
500 m Upstream | 94.0 ± 11.8a | 90.4 ± 12.1a | 92.2 | 2.76 | 212 ± 10.0a | 203 ± 12.7a | 208 | 3.07 | 91.1 ± 56.6a | 212 ± 10.0a | 152 | 56.4 | ||||||||
Discharge point | 90.1 ± 12.9a | 92.1 ± 13.6a | 91.1 | 1.55 | 195 ± 12.1a | 194 ± 16.3a | 195 | 0.36 | 243 ± 89.6a | 212 ± 10.0a | 228 | 9.6 | ||||||||
500 m Downstream | 87.3 ± 11.8a | 82.7 ± 11.8a | 85.0 | 3.83 | 196 ± 11.2a | 185 ± 14.1a | 191 | 4.08 | 263 ± 67.6a | 212 ± 10.0a | 238 | 15.2 | ||||||||
1,000 m Downstream | 83.1 ± 11.9a | 83.0 ± 17.2a | 83.1 | 0.09 | 194 ± 11.2a | 190 ± 14.4a | 192 | 1.47 | 246 ± 36.2a | 212 ± 10.0a | 229 | 10.5 | ||||||||
2,000 m Downstream | 84.2 ± 14.6a | 82.3 ± 11.9a | 83.3 | 1.61 | 194 ± 12.5a | 186 ± 13.5a | 190 | 2.98 | 249 ± 40.6a | 212 ± 10.0a | 231 | 11.4 | ||||||||
Control (22 km away) | 96.3 ± 13.8a | 94.7 ± 14.7a | 95.5 | 1.18 | 219 ± 13.3a | 213 ± 15.4a | 216 | 1.96 | 22.3 ± 7.16a | 28.7 ± 9.07a | 25.5 | 17.7 |
Sampling locations . | Temperature (°C) . | pH . | DO (mg L−1) . | EC (μS cm−1) . | TDS (mg L−1) . | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | |
500 m Upstream | 26.6 ± 1.37cd | 26.0 ± 0.88d | 26.3 | 1.61 | 6.43 ± 0.33c | 6.87 ± 0.14b | 6.65 | 4.68 | 6.56 ± 0.89a | 6.70 ± 0.80a | 6.63 | 1.49 | 134 ± 59b | 111 ± 23b | 123 | 13.3 | 118 ± 28a | 112 ± 15a | 115 | 3.69 |
Discharge point | 27.8 ± 0.79abc | 26.6 ± 1.12cd | 27.2 | 3.12 | 7.41 ± 0.30d | 7.35 ± 0.24a | 7.38 | 0.57 | 6.56 ± 0.54a | 6.16 ± 1.42a | 6.36 | 4.45 | 382 ± 88a | 397 ± 45a | 390 | 2.72 | 105 ± 15b | 101 ± 17b | 103 | 2.75 |
500 m Downstream | 28.6 ± 0.81ab | 26.6 ± 1.03cd | 27.6 | 5.12 | 7.21 ± 0.20a | 7.32 ± 0.20a | 7.27 | 1.07 | 6.99 ± 0.98a | 6.45 ± 1.21a | 6.72 | 5.68 | 393 ± 94a | 361 ± 69a | 377 | 6.00 | 109 ± 12b | 102 ± 19b | 106 | 4.69 |
1,000 m Downstream | 29.2 ± 1.57a | 26.9 ± 1.57bcd | 28.1 | 5.80 | 7.29 ± 0.30a | 7.35 ± 0.19a | 7.32 | 0.58 | 6.94 ± 0.59a | 6.43 ± 1.50a | 6.69 | 5.39 | 390 ± 86a | 353 ± 59a | 372 | 7.04 | 111 ± 11b | 107 ± 24b | 109 | 2.59 |
2,000 m Downstream | 29.8 ± 2.21a | 27.0 ± 1.26bcd | 28.4 | 6.97 | 7.41 ± 0.27a | 7.41 ± 0.24a | 7.41 | 0.00 | 7.00 ± 0.54a | 6.41 ± 1.23a | 6.71 | 6.22 | 394 ± 85a | 374 ± 78a | 384 | 3.68 | 110 ± 12b | 103 ± 14b | 107 | 4.65 |
Control (22 km away) | 27.1 ± 1.74bcd | 26.1 ± 0.66d | 26.6 | 2.66 | 6.35 ± 0.40c | 6.77 ± 0.25c | 6.56 | 4.53 | 6.13 ± 0.62a | 6.59 ± 0.98a | 6.36 | 5.11 | 40.8 ± 30c | 40.1 ± 10c | 40.5 | 1.22 | 123 ± 15a | 118 ± 15a | 121 | 2.93 |
Sampling locations . | TSS (mg L−1) . | TS (mg L−1) . | TH (mg L−1) . | . | . | . | . | . | . | . | . | |||||||||
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | . | . | . | . | . | . | . | . | |
500 m Upstream | 94.0 ± 11.8a | 90.4 ± 12.1a | 92.2 | 2.76 | 212 ± 10.0a | 203 ± 12.7a | 208 | 3.07 | 91.1 ± 56.6a | 212 ± 10.0a | 152 | 56.4 | ||||||||
Discharge point | 90.1 ± 12.9a | 92.1 ± 13.6a | 91.1 | 1.55 | 195 ± 12.1a | 194 ± 16.3a | 195 | 0.36 | 243 ± 89.6a | 212 ± 10.0a | 228 | 9.6 | ||||||||
500 m Downstream | 87.3 ± 11.8a | 82.7 ± 11.8a | 85.0 | 3.83 | 196 ± 11.2a | 185 ± 14.1a | 191 | 4.08 | 263 ± 67.6a | 212 ± 10.0a | 238 | 15.2 | ||||||||
1,000 m Downstream | 83.1 ± 11.9a | 83.0 ± 17.2a | 83.1 | 0.09 | 194 ± 11.2a | 190 ± 14.4a | 192 | 1.47 | 246 ± 36.2a | 212 ± 10.0a | 229 | 10.5 | ||||||||
2,000 m Downstream | 84.2 ± 14.6a | 82.3 ± 11.9a | 83.3 | 1.61 | 194 ± 12.5a | 186 ± 13.5a | 190 | 2.98 | 249 ± 40.6a | 212 ± 10.0a | 231 | 11.4 | ||||||||
Control (22 km away) | 96.3 ± 13.8a | 94.7 ± 14.7a | 95.5 | 1.18 | 219 ± 13.3a | 213 ± 15.4a | 216 | 1.96 | 22.3 ± 7.16a | 28.7 ± 9.07a | 25.5 | 17.7 |
Discharge pt, discharge point (point at which the cement effluents are discharged into the receiving river); RSD, relative standard deviation.
Similar alphabets along the column indicate no statistical difference at p > 0.05 according to the Duncan multiple range test.
Seasonal variations of metals in the Akinbo River (N = 72)
Sampling locations . | Na (mg L−1) . | K (mg L−1) . | Ca (mg L−1) . | Mg (mg L−1) . | Fe (mg L−1) . | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | |
500 m Upstream | 10.4 ± 3.42a | 9.23 ± 3.14a | 9.82 | 8.43 | 2.44 ± 1.87a | 1.38 ± 0.57a | 1.91 | 39.2 | 7.31 ± 4.29a | 7.82 ± 3.00a | 7.57 | 4.77 | 3.07 ± 2.88a | 3.25 ± 1.74a | 3.16 | 4.03 | 2.45 ± 0.69b | 2.66 ± 0.45b | 2.56 | 5.81 |
Discharge point | 10.1 ± 2.74a | 9.33 ± 2.23a | 9.72 | 5.60 | 1.07 ± 0.49a | 1.98 ± 2.45a | 1.53 | 42.2 | 96.0 ± 52.7a | 74.5 ± 18.9a | 85.25 | 17.8 | 6.07 ± 5.48a | 11.1 ± 6.06a | 8.59 | 41.4 | 0.33 ± 0.24c | 0.71 ± 0.63c | 0.52 | 51.7 |
500 m Downstream | 10.4 ± 2.81a | 8.77 ± 3.14a | 9.59 | 12.0 | 1.12 ± 0.45a | 2.18 ± 2.55a | 1.65 | 45.4 | 80.7 ± 53.0a | 64.6 ± 19.3a | 72.65 | 15.7 | 5.10 ± 4.44a | 9.27 ± 4.71a | 7.19 | 41.0 | 0.45 ± 0.23c | 0.68 ± 0.59c | 0.57 | 28.8 |
1,000 m Downstream | 10.5 ± 2.54a | 9.33 ± 2.63a | 9.92 | 8.34 | 1.12 ± 0.45a | 2.30 ± 2.72a | 1.71 | 48.8 | 83.0 ± 47.1a | 64.3 ± 19.6a | 73.65 | 18.0 | 5.05 ± 4.46a | 9.64 ± 5.25a | 7.35 | 44.2 | 0.35 ± 0.18c | 0.75 ± 0.66c | 0.55 | 51.4 |
2,000 m Downstream | 10.3 ± 2.97a | 8.95 ± 1.75a | 9.63 | 9.92 | 1.05 ± 0.47a | 1.72 ± 1.18a | 1.39 | 34.2 | 82.2 ± 40.5a | 56.8 ± 20.4a | 69.50 | 25.8 | 4.88 ± 5.22a | 8.84 ± 5.01a | 6.86 | 40.8 | 0.65 ± 0.40c | 0.66 ± 0.59c | 0.66 | 1.08 |
Control (22 km away) | 6.02 ± 2.86a | 6.55 ± 2.15a | 6.29 | 5.96 | 1.34 ± 1.34a | 1.82 ± 1.63a | 1.58 | 21.5 | 3.31 ± 1.93a | 3.94 ± 3.44a | 3.63 | 12.3 | 0.67 ± 0.57a | 1.16 ± 0.72a | 0.92 | 37.9 | 2.45 ± 0.47ab | 3.35 ± 0.62a | 2.90 | 21.9 |
Sampling locations . | Cu (μg L−1) . | Pb (μg L−1) . | Mn (μg L−1) . | Cr (μg L−1) . | Zn (μg L−1) . | |||||||||||||||
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | |
500 m Upstream | 41.0 ± 0.00a | 34.0 ± 6.00a | 37.5 | 13.2 | 19.0 ± 0.00ab | 15.0 ± 0.00ab | 17.0 | 16.6 | 275 ± 243a | 153 ± 190ab | 214 | 40.3 | 13.0 ± 2.00a | 20.0 ± 10.0a | 16.5 | 30.0 | 18.0 ± 21.0a | 30.0 ± 23.0a | 24.0 | 35.4 |
Discharge point | 26.0 ± 0.00b | 8.00 ± 5.00b | 17.0 | 74.9 | 53.0 ± 0.00a | 15.0 ± 0.00ab | 34.0 | 79.0 | 42.0 ± 32.0b | 56.0 ± 29.0b | 49.0 | 20.2 | 19.0 ± 8.00a | 17.0 ± 6.00a | 18.0 | 7.86 | 46.0 ± 26.0a | 40.0 ± 22.0a | 43.0 | 9.87 |
500 m Downstream | 12.0 ± 9.00b | 20.0 ± 0.00b | 16.0 | 35.4 | 3.00 ± 0.00ab | 25.0 ± 5.00ab | 14.0 | 111 | 70.0 ± 90.0b | 178 ± 150ab | 124 | 61.6 | 11.0 ± 3.00a | 16.0 ± 7.00a | 13.5 | 26.2 | 58.0 ± 29.0a | 28.0 ± 17.0a | 43.0 | 49.3 |
1,000 m Downstream | 29.0 ± 7.00b | 18.0 ± 9.00b | 23.5 | 33.1 | 41.0 ± 0.00a | 36.0 ± 24.0a | 38.5 | 9.2 | 58.0 ± 77.0b | 85.0 ± 99.0b | 71.5 | 26.7 | 15.0 ± 10.0a | 16.0 ± 8.00a | 15.5 | 4.56 | 45.0 ± 32.0a | 30.0 ± 17.0a | 37.5 | 28.3 |
2,000 m Downstream | 22.0 ± 2.00b | 27.0 ± 0.00b | 24.5 | 14.4 | 35.0 ± 0.00a | 26.0 ± 1.00ab | 30.5 | 20.9 | 56.0 ± 43.0b | 58.0 ± 56.0b | 57.0 | 2.48 | 17.0 ± 11.0a | 12.0 ± 4.00a | 14.5 | 24.4 | 53.0 ± 29.0a | 34.0 ± 22.0a | 43.5 | 30.9 |
Control (22 km away) | 19.0 ± 8.00ab | 12.0 ± 1.00b | 15.5 | 31.9 | 8.00 ± 0.00ab | 10.0 ± 0.00b | 9.00 | 15.7 | 75.0 ± 16.0b | 88.0 ± 47.0b | 81.5 | 11.3 | 8.00 ± 2.00a | 18.0 ± 15.00a | 13.0 | 54.4 | 40.0 ± 16.0a | 38.0 ± 24.0a | 39.0 | 3.63 |
Sampling locations . | Na (mg L−1) . | K (mg L−1) . | Ca (mg L−1) . | Mg (mg L−1) . | Fe (mg L−1) . | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | |
500 m Upstream | 10.4 ± 3.42a | 9.23 ± 3.14a | 9.82 | 8.43 | 2.44 ± 1.87a | 1.38 ± 0.57a | 1.91 | 39.2 | 7.31 ± 4.29a | 7.82 ± 3.00a | 7.57 | 4.77 | 3.07 ± 2.88a | 3.25 ± 1.74a | 3.16 | 4.03 | 2.45 ± 0.69b | 2.66 ± 0.45b | 2.56 | 5.81 |
Discharge point | 10.1 ± 2.74a | 9.33 ± 2.23a | 9.72 | 5.60 | 1.07 ± 0.49a | 1.98 ± 2.45a | 1.53 | 42.2 | 96.0 ± 52.7a | 74.5 ± 18.9a | 85.25 | 17.8 | 6.07 ± 5.48a | 11.1 ± 6.06a | 8.59 | 41.4 | 0.33 ± 0.24c | 0.71 ± 0.63c | 0.52 | 51.7 |
500 m Downstream | 10.4 ± 2.81a | 8.77 ± 3.14a | 9.59 | 12.0 | 1.12 ± 0.45a | 2.18 ± 2.55a | 1.65 | 45.4 | 80.7 ± 53.0a | 64.6 ± 19.3a | 72.65 | 15.7 | 5.10 ± 4.44a | 9.27 ± 4.71a | 7.19 | 41.0 | 0.45 ± 0.23c | 0.68 ± 0.59c | 0.57 | 28.8 |
1,000 m Downstream | 10.5 ± 2.54a | 9.33 ± 2.63a | 9.92 | 8.34 | 1.12 ± 0.45a | 2.30 ± 2.72a | 1.71 | 48.8 | 83.0 ± 47.1a | 64.3 ± 19.6a | 73.65 | 18.0 | 5.05 ± 4.46a | 9.64 ± 5.25a | 7.35 | 44.2 | 0.35 ± 0.18c | 0.75 ± 0.66c | 0.55 | 51.4 |
2,000 m Downstream | 10.3 ± 2.97a | 8.95 ± 1.75a | 9.63 | 9.92 | 1.05 ± 0.47a | 1.72 ± 1.18a | 1.39 | 34.2 | 82.2 ± 40.5a | 56.8 ± 20.4a | 69.50 | 25.8 | 4.88 ± 5.22a | 8.84 ± 5.01a | 6.86 | 40.8 | 0.65 ± 0.40c | 0.66 ± 0.59c | 0.66 | 1.08 |
Control (22 km away) | 6.02 ± 2.86a | 6.55 ± 2.15a | 6.29 | 5.96 | 1.34 ± 1.34a | 1.82 ± 1.63a | 1.58 | 21.5 | 3.31 ± 1.93a | 3.94 ± 3.44a | 3.63 | 12.3 | 0.67 ± 0.57a | 1.16 ± 0.72a | 0.92 | 37.9 | 2.45 ± 0.47ab | 3.35 ± 0.62a | 2.90 | 21.9 |
Sampling locations . | Cu (μg L−1) . | Pb (μg L−1) . | Mn (μg L−1) . | Cr (μg L−1) . | Zn (μg L−1) . | |||||||||||||||
Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | Dry . | Wet . | Mean . | RSD . | |
500 m Upstream | 41.0 ± 0.00a | 34.0 ± 6.00a | 37.5 | 13.2 | 19.0 ± 0.00ab | 15.0 ± 0.00ab | 17.0 | 16.6 | 275 ± 243a | 153 ± 190ab | 214 | 40.3 | 13.0 ± 2.00a | 20.0 ± 10.0a | 16.5 | 30.0 | 18.0 ± 21.0a | 30.0 ± 23.0a | 24.0 | 35.4 |
Discharge point | 26.0 ± 0.00b | 8.00 ± 5.00b | 17.0 | 74.9 | 53.0 ± 0.00a | 15.0 ± 0.00ab | 34.0 | 79.0 | 42.0 ± 32.0b | 56.0 ± 29.0b | 49.0 | 20.2 | 19.0 ± 8.00a | 17.0 ± 6.00a | 18.0 | 7.86 | 46.0 ± 26.0a | 40.0 ± 22.0a | 43.0 | 9.87 |
500 m Downstream | 12.0 ± 9.00b | 20.0 ± 0.00b | 16.0 | 35.4 | 3.00 ± 0.00ab | 25.0 ± 5.00ab | 14.0 | 111 | 70.0 ± 90.0b | 178 ± 150ab | 124 | 61.6 | 11.0 ± 3.00a | 16.0 ± 7.00a | 13.5 | 26.2 | 58.0 ± 29.0a | 28.0 ± 17.0a | 43.0 | 49.3 |
1,000 m Downstream | 29.0 ± 7.00b | 18.0 ± 9.00b | 23.5 | 33.1 | 41.0 ± 0.00a | 36.0 ± 24.0a | 38.5 | 9.2 | 58.0 ± 77.0b | 85.0 ± 99.0b | 71.5 | 26.7 | 15.0 ± 10.0a | 16.0 ± 8.00a | 15.5 | 4.56 | 45.0 ± 32.0a | 30.0 ± 17.0a | 37.5 | 28.3 |
2,000 m Downstream | 22.0 ± 2.00b | 27.0 ± 0.00b | 24.5 | 14.4 | 35.0 ± 0.00a | 26.0 ± 1.00ab | 30.5 | 20.9 | 56.0 ± 43.0b | 58.0 ± 56.0b | 57.0 | 2.48 | 17.0 ± 11.0a | 12.0 ± 4.00a | 14.5 | 24.4 | 53.0 ± 29.0a | 34.0 ± 22.0a | 43.5 | 30.9 |
Control (22 km away) | 19.0 ± 8.00ab | 12.0 ± 1.00b | 15.5 | 31.9 | 8.00 ± 0.00ab | 10.0 ± 0.00b | 9.00 | 15.7 | 75.0 ± 16.0b | 88.0 ± 47.0b | 81.5 | 11.3 | 8.00 ± 2.00a | 18.0 ± 15.00a | 13.0 | 54.4 | 40.0 ± 16.0a | 38.0 ± 24.0a | 39.0 | 3.63 |
Discharge pt, discharge point (point at which the cement effluents are discharged into the receiving river); RSD, relative standard deviation.
Similar alphabets along the column indicate no statistical difference at p > 0.05 according to the Duncan multiple range test.
The mean pH values of the Akinbo River ranged from 6.87 to 7.35 in the wet season and 6.43–7.41 in the dry season, indicating slightly acidic to neutral conditions. All the pH values fell within the WHO guideline range of 6.5–8.5 (WHO 2017a). The season appeared to have minimal or no impact on the stream's pH value. Bilen (2010) documented a pH range of 7.0–8.5 for surface water near the Southdown Portland cement factory in Lyons, Colorado, while Olaleye (2005) noted pH ranges of 6.63–7.73 (wet season) and 6.21–7.91 (dry season) in the effluents around the Ewekoro cement factory. The pH variation of 7.89–7.97 was recently reported by Abdus-Salam & Adeoye (2019) in the Akinbo River in the vicinity of the cement industry.
DO values did not show any significant difference with sampling distances from the factory. However, the DO values were slightly higher during the dry season than the wet season. The DO value range for the dry season was from 6.56 to 7.0 mg L−1 and for the wet season, 6.16–6.7 mg L−1, which were within the acceptable limit of 4.0 mg L−1 set by the Nigerian Federal Ministry of Water Resources as the permissible level for surface water quality criteria for public supplies (FRN 2000).
TDS, TSS, and total solids (TS) values did not show any significant differences with sampling distances from the factory. Nevertheless, elevated levels of TDS, TSS, and TS exceeding the WHO (2017a) standards were observed in the same Akinbo River near the cement factory (Abdus-Salam & Adeoye 2019). EC did not vary between the two seasons but showed higher values at the factory effluent discharge point on the stream, similar to the values reported by Abdus-Salam & Adeoye (2019). However, higher EC values (488–1,870 μS cm−1) were reported by Mbaka (2023) in water samples collected from Athi River, Kenya, near a cement industry.
TH followed a similar trend to the EC concentration of the river. However, a notable rise in TH was measured at the sampling points immediately after the factory effluent discharge point.
Table 2 presents the levels of metals in surface water samples. The concentrations of K, Na, Mg, and Ca were indicative of their natural presence in the surface water, with elevated levels observed at the discharge point downstream. These values were consistent with those documented for the same area by Olaleye (2005). Notably, the increased levels of K and Mg during the wet season could potentially be attributed to the influx of water from erosion.
The Pb values of the stream were below the WHO guideline value of 0.05 mg L−1 (WHO 2017a), except at the discharge point of the factory effluent during the dry season. Cu and Zn concentrations were also lower than the respective guideline values of 1.0 and 5.0 mg L−1 (WHO 2017a). At some sampling sites, Mn, Cr, and Fe have values slightly higher than the WHO guideline values of 0.1, 0.005, and 0.3 mg L−1, respectively (WHO 2017a). The heavy metals data (Cr, Cu, Pb, Mn, Fe, and Zn) were generally low with no seasonal significance, except for Fe. Many of these metals are emitted from anthropogenic activities such as traffic, industries, incineration, and combustion of fossil fuels (Taiwo et al. 2014); however, their low occurrence in the Akinbo stream indicated minimal inputs from the cement factory. In contrast, Mbaka (2023) documented Pb values exceeding the WHO permissible limit in the Athi River around a cement industry in Kenya, but the Cr concentrations were within the threshold limits at most of the sampling sites.
The Pearson correlation coefficients of water parameters are presented in Supplementary Table S2. There was a strong positive correlation between EC and TH (r2 = 0.997; p < 0.01), indicating a probable influence from the salts of Mg and Ca, which are essential constituents of cement dust. The study of Mbaka (2023) had shown strong correlation between cement industry in Kenya and EC. A strong positive relationship was also observed between Ca and Mg (r2 = 0.841; p < 0.05). Ca is a major tracer element for cement (Taiwo et al. 2014).
A high significant correlation between Zn and Cu (r2 = 0.878; p < 0.05) and Zn and Mn (r2 = 0.899; p < 0.05) might indicate a common source of emission, probably from other anthropogenic activities aside from cement production, since these metals were anti-correlated with Ca and Mg. Previous studies have adopted Zn and Cu as fingerprints for traffic pollution (Taiwo et al. 2014, 2017).
Vegetation parameters
The results of vegetation parameters along the sampling distance from the cement factory are presented in Table 3 and Supplementary Table S3. The number of trees increased significantly from 48 to 148 trees per hectare up to 3 km away from the factory, where another dust-generating activity (clay factory) was located. This increase can be attributed partly to the effect of the cement dust on the vegetation and/or partly to deforestation work carried out around the factory site during the construction of the new factory in 2001. The land areas around the factory were generally affected during the construction work, as industrialisation often involves deforestation. The areas farther away were beyond the factory-acquired land and thus remained protected. In the study by Salami et al. (2002) around the Ewekoro factory, a gradual increase in the number of trees was also observed, from 38 to 218 trees per hectare, but this increase became insignificant after 6 km away from the factory location, where the highest density was found.
Vegetation parameters in relation to distance from dust pollution source for wet seasons 2005 and 2006
Distance from factory (km) . | No of trees in 25 × 25 m plot . | No of trees per hectare . | Mean DBH/plot (cm) . | Mean DBH /hectare . | Mean TBA/plot (m2/hectare) πD2/4 . | Mean TBA/hectare (m2/hectare) πD2/4 . | Mean tree height/plot (m) . | Mean tree height/hectare (m) . | Leaf abundance/plant (average of 10 stalks) . | Comments on vegetation . | |
---|---|---|---|---|---|---|---|---|---|---|---|
Cassava . | Sugarcane . | ||||||||||
0 | 3.0 ± 0.0d | 48d | 40.67 ± 22.20a | 650.72a | 0.17 ± 0.17a | 2.70a | 11.73 ± 4.79a | 187.68a | 36.25 ± 1.27e | 11.4 ± 1.66d | Dusty leaves, yellowish patches on the leaf surfaces, folding at the edges. |
1 | 7.0 ± 0.0b | 112b | 24.64 ± 14.54d | 394.24d | 0.07 ± 0.08d | 1.04d | 7.91 ± 3.40b | 126.56b | 48.75 ± 9.21d | 12.5 ± 0.77c | Improved surface areas and number of leaves, leaves more greenish. |
2 | 8.0 ± 0.0a | 128a | 27.90 ± 15.53c | 464.40c | 0.08 ± 0.08c | 1.28c | 6.95 ± 2.88c | 111.20c | 70.00 ± 10.11c | 13.0 ± 1.53b | Leaves on cassava more, fresher, wider and less dusty. |
3 | 4.5 ± 0.5c | 80c | 18.32 ± 5.03e | 293.12e | 0.03 ± 0.01e | 0.45e | 7.32 ± 1.50c | 117.12c | 82.75 ± 8.77b | 13.0 ± 0.85b | Number of leaves on cassava more appreciable but not on sugarcane. Ogun State Clay bricks factory close to 3 km from Ewekoro. |
Control Site 22 km | 7.0 ± 0.0b | 112b | 38.56 ± 11.55b | 616.96b | 0.13 ± 0.07b | 2.03b | 7.83 ± 1.08b | 125.28b | 125.00 ± 12.01a | 14.0 ± 1.10a | Fresher leaves, wider surfaces, no dust impregnation, no yellowish patches. Leaves mostly abundant on the stalks. |
Distance from factory (km) . | No of trees in 25 × 25 m plot . | No of trees per hectare . | Mean DBH/plot (cm) . | Mean DBH /hectare . | Mean TBA/plot (m2/hectare) πD2/4 . | Mean TBA/hectare (m2/hectare) πD2/4 . | Mean tree height/plot (m) . | Mean tree height/hectare (m) . | Leaf abundance/plant (average of 10 stalks) . | Comments on vegetation . | |
---|---|---|---|---|---|---|---|---|---|---|---|
Cassava . | Sugarcane . | ||||||||||
0 | 3.0 ± 0.0d | 48d | 40.67 ± 22.20a | 650.72a | 0.17 ± 0.17a | 2.70a | 11.73 ± 4.79a | 187.68a | 36.25 ± 1.27e | 11.4 ± 1.66d | Dusty leaves, yellowish patches on the leaf surfaces, folding at the edges. |
1 | 7.0 ± 0.0b | 112b | 24.64 ± 14.54d | 394.24d | 0.07 ± 0.08d | 1.04d | 7.91 ± 3.40b | 126.56b | 48.75 ± 9.21d | 12.5 ± 0.77c | Improved surface areas and number of leaves, leaves more greenish. |
2 | 8.0 ± 0.0a | 128a | 27.90 ± 15.53c | 464.40c | 0.08 ± 0.08c | 1.28c | 6.95 ± 2.88c | 111.20c | 70.00 ± 10.11c | 13.0 ± 1.53b | Leaves on cassava more, fresher, wider and less dusty. |
3 | 4.5 ± 0.5c | 80c | 18.32 ± 5.03e | 293.12e | 0.03 ± 0.01e | 0.45e | 7.32 ± 1.50c | 117.12c | 82.75 ± 8.77b | 13.0 ± 0.85b | Number of leaves on cassava more appreciable but not on sugarcane. Ogun State Clay bricks factory close to 3 km from Ewekoro. |
Control Site 22 km | 7.0 ± 0.0b | 112b | 38.56 ± 11.55b | 616.96b | 0.13 ± 0.07b | 2.03b | 7.83 ± 1.08b | 125.28b | 125.00 ± 12.01a | 14.0 ± 1.10a | Fresher leaves, wider surfaces, no dust impregnation, no yellowish patches. Leaves mostly abundant on the stalks. |
DBH, diameter at breast height; TBA, tree basal area.
Means with similar alphabets (in superscript) along the columns are not significantly different at p > 0.05.
The average tree DBH followed a similar pattern to the number of trees, albeit with fewer trees at 0 km. One tree stood out for its significant size, having been protected from farmers' activities, which inflated the diameter measurement for this particular plot. A noticeable increase was observed as the distance from the factory extended up to 3 km. Basal area values derived from these breast height measurements were generally modest and comparable to those reported in a previous study (Salami et al. 2002).
It was observed that tree species were less than 40 years old and that cement dust pollution did not prevent the appearance and growth of these tree species, although it might have affected their growth rate. This suggested that the impact of land use, especially the farming system in the area, reinforced the effect of factory pollution on the vegetation in the area, which is similar to the findings of Du et al. (1998) and Saravanan & Appavu (1998). A wide variety of tree species have been studied for their response to dust. Dust may cause physical injury to tree leaves and bark, reduced fruit setting, and a general reduction in growth (Squires 2016). Details of injuries to a range of tree species from cement kiln dust have been described elsewhere in Lepeduš et al. (2003). The dust forms a hard crystalline crust on the leaf surface, which dissolves, releasing a solution of calcium hydroxide into the intercellular spaces. This causes cell plasmolysis and death. Cement dust deposition can lead to growth reductions for many species (Iqbal & Shafiq 2001; Sett 2017).
The quantity of dust that affects trees is much more difficult to ascertain than for crops. Brandt & Rhoades (1972) measured the cement/lime dust deposition rate that could affect trees and found that the rate was high compared with those of many crops previously studied. The study concluded that it was not possible to determine a critical level of deposition that could initiate the effects described for crops. It is also uncertain how long some of the physiological responses could impact the health of trees. Flückiger et al. (1979) found that, while 1 mg cm−2 of dust was necessary to cause a decrease in stomatal diffusive resistance in Popolus tremula, only 0.5 mg cm−2 was necessary to increase leaf temperature.
Following a similar pattern to the tree data, the leaf abundance and chlorophyll contents of cassava (M. esculenta) and sugarcane (S. officinarum) increased significantly up to 3 km away from the factory. The most positive result was observed for the leaf abundance at the control site, 22 km away from the factory. Some of the chlorophyll content values of plants around the study area insignificantly exceeded those from the control site, which is similar to the results reported by Prasad & Inamdar (1990) and Iqbal & Shafiq (2001). This may have negative effects on crop yields and consequently affect the agricultural activities within the vicinity of the factory. Raajasubramanian et al. (2011) reported that cement dust caused a reduction in the growth and yield of groundnuts (Arachis hypogaea L.).
The variations in the chlorophyll contents of cassava and sugarcane could be explained by their susceptibilities to pollutants. Plant responses to pollutants varied between species of a given genus and between varieties within a given species. Plants do not necessarily show similar susceptibility to different pollutants. The presence of heavy metals in cement dust could also have played a significant role in the various metabolic processes of plants (Abdel-Rahman & Ibrahim 2012; Mutlu et al. 2013).
Health assessment
Tables 4 and 5 display the common health issues among the factory workers, the neighbouring villagers in Itori, and the residents of Obada Oko (chosen as the control site). Data on the ailments were gathered from the clinic records in Itori and Obada Oko towns. Itori is approximately 2 km from the factory plant, while Obada Oko is situated about 10 km away from the Ewekoro cement plant.
Health data of reported URT, malaria, and GIT disorders in the selected areas
Year . | . | URT infections . | Malaria infections . | GIT disorders . | ||||||
---|---|---|---|---|---|---|---|---|---|---|
FW . | Obada . | NV . | FW . | Obada . | NV . | FW . | Obada . | NV . | ||
2003 | Frequency | 1,611 | 34 | 148 | 1,486 | 43 | 630 | 514 | 22 | 117 |
Percent | 90 | 2 | 8 | 69 | 2 | 29 | 79 | 3 | 18 | |
2004 | Frequency | 1,278 | 50 | 165 | 1,782 | 48 | 625 | 540 | 25 | 95 |
Percent | 86 | 3 | 11 | 73 | 2 | 25 | 82 | 4 | 14 | |
2005 | Frequency | 1,300 | 79 | 142 | 1,710 | 54 | 693 | 670 | 26 | 73 |
Percent | 85 | 5 | 9 | 70 | 2 | 28 | 87 | 3 | 9 | |
2006 | Frequency | 1,381 | 45 | 190 | 1,813 | 56 | 607 | 475 | 32 | 148 |
Percent | 85 | 3 | 12 | 73 | 2 | 25 | 73 | 5 | 23 |
Year . | . | URT infections . | Malaria infections . | GIT disorders . | ||||||
---|---|---|---|---|---|---|---|---|---|---|
FW . | Obada . | NV . | FW . | Obada . | NV . | FW . | Obada . | NV . | ||
2003 | Frequency | 1,611 | 34 | 148 | 1,486 | 43 | 630 | 514 | 22 | 117 |
Percent | 90 | 2 | 8 | 69 | 2 | 29 | 79 | 3 | 18 | |
2004 | Frequency | 1,278 | 50 | 165 | 1,782 | 48 | 625 | 540 | 25 | 95 |
Percent | 86 | 3 | 11 | 73 | 2 | 25 | 82 | 4 | 14 | |
2005 | Frequency | 1,300 | 79 | 142 | 1,710 | 54 | 693 | 670 | 26 | 73 |
Percent | 85 | 5 | 9 | 70 | 2 | 28 | 87 | 3 | 9 | |
2006 | Frequency | 1,381 | 45 | 190 | 1,813 | 56 | 607 | 475 | 32 | 148 |
Percent | 85 | 3 | 12 | 73 | 2 | 25 | 73 | 5 | 23 |
URT, upper-respiratory tract infection (cough, catarrh, tonsillitis, bronchitis, and asthma); GIT, gastrointestinal tract disorders (peptic ulcer, typhoid, gastroenteritis, and dysentery); FW, factory workers; NV, neighbouring villagers.
Health data of reported CVD, arthritis, and dermatitis in the selected areas
Year . | . | CVDs . | Arthritis patients . | Dermatitis infections . | ||||||
---|---|---|---|---|---|---|---|---|---|---|
FW . | Obada . | NV . | FW . | Obada . | NV . | FW . | Obada . | NV . | ||
2003 | Frequency | 71 | 12 | 45 | 763 | 1 | 12 | 453 | 10 | 8 |
Percent | 55 | 9 | 35 | 98 | 0 | 2 | 96 | 2 | 2 | |
2004 | Frequency | 65 | 9 | 33 | 684 | 8 | 10 | 268 | 11 | 11 |
Percent | 61 | 8 | 31 | 97 | 1 | 1 | 92 | 4 | 4 | |
2005 | Frequency | 41 | 11 | 44 | 565 | 10 | 172 | 9 | ||
Percent | 43 | 11 | 46 | 98 | 2 | 0 | 95 | 5 | 0 | |
2006 | Frequency | 25 | 6 | 47 | 415 | 9 | 14 | 182 | 20 | 6 |
Percent | 32 | 8 | 60 | 95 | 2 | 3 | 88 | 10 | 3 |
Year . | . | CVDs . | Arthritis patients . | Dermatitis infections . | ||||||
---|---|---|---|---|---|---|---|---|---|---|
FW . | Obada . | NV . | FW . | Obada . | NV . | FW . | Obada . | NV . | ||
2003 | Frequency | 71 | 12 | 45 | 763 | 1 | 12 | 453 | 10 | 8 |
Percent | 55 | 9 | 35 | 98 | 0 | 2 | 96 | 2 | 2 | |
2004 | Frequency | 65 | 9 | 33 | 684 | 8 | 10 | 268 | 11 | 11 |
Percent | 61 | 8 | 31 | 97 | 1 | 1 | 92 | 4 | 4 | |
2005 | Frequency | 41 | 11 | 44 | 565 | 10 | 172 | 9 | ||
Percent | 43 | 11 | 46 | 98 | 2 | 0 | 95 | 5 | 0 | |
2006 | Frequency | 25 | 6 | 47 | 415 | 9 | 14 | 182 | 20 | 6 |
Percent | 32 | 8 | 60 | 95 | 2 | 3 | 88 | 10 | 3 |
CVD, cardiovascular diseases (hypertension, angina pain, and palpitation); Arthritis, muscular and joint pains; FW, factory worker; NV, neighbouring villager; Dermatitis, contact and infectious skin diseases, nail infections (paronychia).
The prevalence of URT infections, which include cough, catarrh, tonsillitis, bronchitis, asthma, dermatitis, and eye problems, was observed to be highest (90%) among the factory workers compared with the neighbouring villages (2%) and the control site at Obada Oko (8%). The high prevalence of URT among the factory workers may be directly linked to exposure to cement dust (Vestbo & Rasmussen 1990). According to Rahmani et al. (2018), the most important occupational hazards for cement workers are allergies and complications related to the respiratory system. URT infections among the factory workers dropped from 90% in 2003 to 85% in 2006. The decrease in the occurrence of URT infections and other dust-inducing ailments among the factory workers can be attributed to several factors. These factors might include: (1) a better campaign and awareness on the use of PPE, (2) the introduction of new production technology as reflected in the new dry system plant, (3) better clinic attention, and retirement of old staff who have spent long years of service with greater exposure to dust, and (4) a better environmental management system in the plant as reflected in the certification of the factory to NIS ISO 14001:2004 in late 2006.
Similarly, malaria and gastrointestinal tract (GIT) disorder cases were also most prevalent among the factory workers. Malaria is a common disease in the tropics and may not be attributable to exposure to cement dust (Heggenhougen et al. 2003). However, GIT disorder may be related to exposure to cement dust through inhalation (Owonikoko et al. 2022).
Koh et al. (2011) linked exposure to cement dust with gastrointestinal cancer. The study by Owonikoko et al. (2022) showed a significant alteration of gastrointestinal secretions that predisposes the GIT to an array of deleterious effects through protein oxidation, antioxidant depletion, and tissue peroxidation in Wistar rats exposed to cement dust.
Cases of CVDs, arthritis, and dermatitis were also prevalent among the factory workers. However, CVD cases were prevalent among the residents of neighbouring villages in 2005 and 2006. The latter may be related to risk factors such as an unhealthy diet, physical inactivity, tobacco use, and the harmful use of alcohol (WHO 2017b). Globally, CVD is the leading cause of death, which has also been linked to air pollution (Taiwo 2016, 2022; Rajagopalan et al. 2018). Data showed that 17.9 million people died from CVDs in 2016, representing 31% of all global deaths (WHO 2017b).
The 2006 data for factory workers revealed a general reduction in the cases of URT (90–85%), GIT (79–73%), CVD (55–32%), arthritis (98–95%), and dermatitis (96–88%). Among these dust-related diseases, only CVD has shown a drastic decline, probably due to the mitigating measures highlighted earlier, especially the use of PPE and a better environmental management system (NIS ISO 14001:2004).
CONCLUSION
This study assessed the environmental impacts of the Portland cement factory in Ewekoro on surface water (Akinbo River), vegetation, and the health status of workers and neighbouring communities. The surface water samples were found to be rich in calcium carbonate and bicarbonate ions, showing a pH range of 6.87–7.35 in the wet season and 6.43–7.41 in the dry season. The physical and chemical parameters of surface water were not significantly altered by the activities of the cement industry. This was reflected in the strong correlations of some water parameters with calcium. Heavy metal values from the Akinbo River were generally low, with minimal impacts from the cement factory. The growth and chlorophyll contents of cassava and sugarcane were affected by the cement dust. The presence of another dust-generating factory less than 3 km away, the state-owned Gateway Brick Company, might have additional effects on the vegetation growth in the area. Almost all the tree species measured in this study have small basal areas and heights.
The number of ailment cases reported in this study highlights the impact of cement dust on the exposed workers and neighbouring villagers around the industry. Although the incidences of CVD, arthritis, and dermatitis decreased between 2003 and 2006, there is a necessity for enhancing the dust control measures implemented by the factory. The occurrences of URT diseases, primarily linked to cement dust, remain elevated despite a 5% reduction. The data collected from the water, vegetation, and medical records underscore the importance of reassessing the dust suppression system employed by the industry to safeguard public health. The limitation of this study may be related to the failure to determine vital water parameters such as Hg, As, and Cd, which are major tracers for cement dust. Furthermore, the inability to statistically link the workers' medical records with cement factory activities is another major setback for this study.
ACKNOWLEDGEMENTS
The authors thank Mr T. Olopade and Mrs E.O. Sorinola of the Department of Environmental Management & Toxicology, Federal University of Agriculture, Abeokuta, for assistance rendered during the analysis.
DEDICATION
This paper is dedicated to the memory of the first author, Dr Orisunmibare Taiwo Agbede, who died in November 2014, four years after completing his Ph.D. programme.
FUNDING
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.
REFERENCES
Author notes
Deceased.