1. Introduction

2. Methodology

2.3. Analysis methods

2.3.1. Pollutant Air Quality Index (AQI)

The Air Quality Index (AQI) is a scale ranging from 0 to 500, providing a measure of air pollution and associated health risks [18]. A higher AQI indicates greater pollution and increased health concerns, with values over 300 signifying hazardous conditions. An AQI value of 50 or below signifies good air quality, while values above 100 are considered unsatisfactory and pose health risks. The AQI is categorized into six levels, each associated with specific health concerns, and color-coded for easy community awareness.

Figure 2. Air Quality Index Basics for Ozone and Particle Pollution [18].

Figure 2. Air Quality Index Basics for Ozone and Particle Pollution [18].

Continuous real-time sampling of ambient air was conducted four times daily—once in the morning and thrice in the afternoon. Subsequently, the acquired data were averaged to derive pollutant emissions, and the averages for each session (morning and afternoon) were recorded separately. This method facilitated ongoing monitoring of air quality, enabling a comprehensive assessment of pollutant levels in the surrounding environment. The AQI is computed as follows:

$${AQI}_{\mathrm{P}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}}=\frac{\mathrm{P}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t} \mathrm{d}\mathrm{a}\mathrm{t}\mathrm{a} \mathrm{r}\mathrm{e}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}{\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{d}}\mathrm{X}10$$

(1)

The AQI pollutant corresponds to the AQI values for PM_2.5 and PM₁₀ emissions. The pollutant data readings represent emissions measured during morning and afternoon sessions. The standard for PM_2.5 is considered equal to the PM₁₀ standard, which is set at 150 μg/m³ [19].

2.3.2. Relationship between exposure to emission and health outcome

Air pollution has been associated with various adverse health effects in early human life, encompassing respiratory, cardiovascular, mental, and perinatal disorders, potentially leading to infant mortality or chronic diseases later in life [20,21,22]. This study employed a case-control study design to analyze the relationship between exposure and health outcomes, leveraging specific advantages of case-control studies compared to other designs. Cases, individuals with the outcome, and controls, those without the outcome, were identified. Respondents' ages were categorized into infant (0-5), young (6-24), middle-aged (25-60), and old (≥ 61) years. Due to the small proportion of the old and infant age groups, only the young and middle-aged groups were considered for odds ratio calculation. Cases and controls were sourced from clinical records, with the case group comprising individuals with respiratory diseases in villages near a cement factory (Sire Berga, Gatira Nabe, Sago, and Bekata), and controls representing those with non-respiratory diseases. Patients from more distant areas (San-baro, Qore Sena, Fite, and Haroo Lemana) were included. Particulate matter and gaseous pollutants were measured in these locations, excluding smokers from the case group. The sample size was determined using Slovene's formula, a random sampling technique. The odds ratio (OR) was utilized to estimate the strength of the association between exposure and outcome in case-control studies.

$$\mathrm{O}\mathrm{d}\mathrm{d}\mathrm{s} \mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o} (\mathrm{O}\mathrm{R})=\frac{\mathrm{O}\mathrm{d}\mathrm{d}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e} \mathrm{a}\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{g} \mathrm{c}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{s}}{\mathrm{O}\mathrm{d}\mathrm{d}\mathrm{s} \mathrm{o}\mathrm{f} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e} \mathrm{a}\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{g} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}$$

(2)

The relative risk provides insight into the increased likelihood of exposed individuals developing cases compared to unexposed individuals. In contrast, the odds ratio is a ratio of two odds. It's important to note that the relative risk is a ratio of two probabilities, where risk represents the likelihood of the outcome of interest among all possible outcomes and it is computed as [23]:

$$\mathrm{R}\mathrm{e}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k} (\mathrm{R}\mathrm{R})=\frac{\mathrm{N}\mathrm{o}.\mathrm{o}\mathrm{f} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{e}\mathrm{d} \mathrm{c}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{s}/\mathrm{N}\mathrm{o} \mathrm{o}\mathrm{f} \mathrm{u}\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{e}\mathrm{d} \mathrm{c}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{s}}{\mathrm{N}\mathrm{o}.\mathrm{o}\mathrm{f} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{e}\mathrm{d} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{e}/\mathrm{N}\mathrm{o} \mathrm{o}\mathrm{f}\mathrm{u}\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{e}\mathrm{d} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}$$

(3)

3. Results and discussion

3.3. Emissions of pollutants

PM_2.5 measurements indicated diverse concentrations among various cement plants, with the Dangote cement plant ranging from 104.8 to 657.6 μg/m³, Kuyu cement plant from 159 to 655 μg/m³, National cement plant from 137 to 656 μg/m³, Ethio cement plant from 141 to 663 μg/m³, Derba cement plant from 137 to 667 μg/m³, Pioneer cement plant from 137 to 814 μg/m³, and Habesha cement plant from 140 to 266 μg/m³. Conversely, Gatira Nabe, Saggo, Sire Berga, and Bekata reported lower values ranging from 188 to 196 μg/m³, 150–152 μg/m³, 167–189 μg/m³, and 113–120 μg/m³, respectively. Similarly, PM₁₀ values exhibited variation among different cement plants, with Dangote cement plant ranging from 195 to 665 μg/m³, Kuyu cement plant from 304 to 765 μg/m³, National cement plant from 227 to 874 μg/m³, Ethio cement plant from 274 to 886 μg/m³, Derba cement plant from 252 to 849 μg/m³, Pioneer cement plant from 245 to 862 μg/m³, and Habesha cement plant from 251 to 868 μg/m³. Conversely, Gatira Nabe, Saggo, Sire Berga, and Bekata reported values between 172 and 181 μg/m³, 234–342 μg/m³, 215–453 μg/m³, and 125–263 μg/m³, respectively.

Ambient air quality concerning both PM_2.5 and PM₁₀ exceeded Ethiopian ambient air quality guidelines (EPA, 2003) in all cement plants and nearby areas, including Gatira Nabe, Saggo, and Sire Berga. Contrastingly, gaseous pollutants like nitrogen dioxide, sulfur dioxide, and carbon dioxide remained within acceptable limits at all sites. In accordance with the AQI classification, the mill area demonstrated hazardous conditions for both PM_2.5 and PM₁₀ emissions. Furthermore, the storage, loading, and coal crusher areas were identified as high-risk zones for workers. Among nearby villages, Gatira Nabe was particularly flagged for health risks. The study underscores a correlation between PM pollutant emissions, health impacts, and proximity to factory units, indicating that emissions increased with closeness. This finding is consistent with prior research [5,25], which consistently emphasized an inverse relationship between PM and distance from the factory.

The emission profiles of pollutants in different production process rooms within the proximity of Dangote cement factory and Muger cement factory (Sanbaro, Qore Sena, Fite, and Haro Lemana) located at distances of 10, 20, 30, and 40 km from both factories were scrutinized (Figure 2). Gaseous emissions across all cement industries were minimal, while particulate matter emissions surpassed permissible standards. Mean emissions of PM_2.5 and PM₁₀ pollutants varied across production rooms, with milling rooms exhibiting higher concentrations, followed by storage and loading, particularly for PM₁₀. This emphasizes increased worker exposure in the milling room, while workers in loading and storage rooms were also impacted by particulate matter. Notably, pollutant concentrations decreased with greater distance from the factories, indicating heightened exposure for communities in closer proximity (Figure 2).

Figure 2. Mean emissions of PM_2.5 (μg/m³) pollutants measured at different distance.

Figure 2. Mean emissions of PM_2.5 (μg/m³) pollutants measured at different distance.

The concentration of PM₁₀ μg/m³ measured at different distance of site were decreasing, when the site of data collection was far-away from the cement factories, that show the community living around the factories were more affected by this particulate matter than those community living at far distance (Figure 3).

The concentration of CO₂ was observed to be high across the selected study areas and distances from the cement factories, while the concentrations of both SO₂ and N₂O were comparatively low in all cement factories. The data presented in Table 3 details the mean emissions of CO₂, N₂O, and SO₂ measured at varying distances from the cement factories, providing insights into the pollutant concentrations during morning and afternoon sampling durations. For a distance of 10 km, the morning CO₂ values ranged from 399 to 419 mg/m³, with Dangote having the highest morning concentration, and afternoon values ranged from 499 to 522 mg/m³. N₂O morning values spanned from 1.143 to 1.312 mg/m³, with Derba exhibiting the highest morning concentration, and afternoon values ranged from 1.324 to 1.562 mg/m³. SO₂ morning values ranged from 0.234 to 0.324 mg/m³, with Derba having the highest morning concentration, and afternoon values ranged from 0.394 to 0.455 mg/m³.

At a distance of 20 km, the morning CO₂ values ranged from 433 to 463 mg/m³, with Pioneer having the highest morning concentration, and afternoon values ranged from 309 to 332 mg/m³. N₂O morning values spanned from 2.21 to 2.36 mg/m³, with Habesha exhibiting the highest morning concentration, and afternoon values ranged from 3.35 to 3.52 mg/m³. SO₂ morning values ranged from 0.4665 to 0.5753 mg/m³, with Habesha having the highest morning concentration, and afternoon values ranged from 0.3987 to 0.5532 mg/m³. For a distance of 30 km, the morning CO₂ values ranged from 269 to 288 mg/m³, with National having the highest morning concentration, and afternoon values ranged from 425 to 453 mg/m³. N₂O morning values spanned from 1.18 to 1.3 mg/m³, with National exhibiting the highest morning concentration, and afternoon values ranged from 1.22 to 1.32 mg/m³. SO₂ morning values ranged from 0.2325 to 0.3015 mg/m³, with National having the highest morning concentration, and afternoon values ranged from 0.2124 to 0.3015 mg/m³. At a distance of 40 km, the morning CO₂ values ranged from 255 to 288 mg/m³, with National having the highest morning concentration, and afternoon values ranged from 266 to 282 mg/m³. N₂O morning values spanned from 1.89 to 2.34 mg/m³, with Pioneer exhibiting the highest morning concentration, and afternoon values ranged from 2.698 to 3.01 mg/m³. SO₂ morning values ranged from 0.29 to 0.42 mg/m³, with National having the highest morning concentration, and afternoon values ranged from 0.49 to 0.58 mg/m³.

Remarkably, the concentrations of CO₂, N₂O, and SO₂ at all distances from the cement factories were consistently below the permissible limits set by Ethiopian standards (Table 3). This finding suggests that the cement factories are operating within the prescribed environmental standards, particularly concerning gaseous pollutant emissions. The study provides valuable insights into the spatial distribution of these pollutants, emphasizing the need for continued monitoring and adherence to environmental regulations in cement production processes.

Numerous research endeavors have explored the connection between wind conditions and the release of air pollutants, establishing a direct relationship [26]. The dispersion of pollutants is notably contingent on factors such as wind speed and direction, with wind dynamics playing a pivotal role in determining the spread of gaseous pollutants [27,28]. Wind speed and direction emerge as critical parameters that significantly impact the diffusion of pollutants, as they facilitate the transportation of varying quantities of pollutants [29].

3.4. Health effects

Patients with various illnesses across diverse distances from all selected health centers indicate that those in proximity to cement factories exhibit a higher incidence of diverse diseases. Conversely, health centers situated farther from cement factories report a lower prevalence of respiratory diseases. This highlights the influence of distance on the observed co-occurrence of different diseases across various health centers. As the distance from health centers to cement factories increases, there is a noticeable decrease in the number of patients with diverse respiratory conditions recorded at each health center (Figure 4).

The prevalence of respiratory diseases across various age groups and health centers reveals distinct patterns, emphasizing the influence of both age and proximity to cement factories. Notably, the age group of 25-60 consistently exhibited the highest prevalence of diseases across all health centers, followed by the age group of 6-24. Conversely, the other two age groups showed a lower susceptibility to these diseases (Table 4). This observation emphasizes the significant impact of age on disease prevalence. Furthermore, the number of recorded patients in health centers varied based on both their geographical location and distance from the cement factories. Health centers located in close proximity to the cement plants reported a significantly higher number of patients compared to those situated farther away. Specifically, Reji Health Center, located 2 km from both Muger and Dangote cement factories, recorded the highest number of patients for all prevalent diseases. This trend was followed by Enchini health center and Enchini primary hospital, situated 5 km from Dangote and 10 km from Muger. The association between health center location and disease prevalence is evident, highlighting the direct impact of proximity to cement factories on community health (Table 4).

Table 4 provides a detailed breakdown of respiratory allergy diseases categorized under different age groups across various health centers in the year 2022. The data indicates that the prevalence of bronchial asthma, chronic bronchitis, skin infection, acute bronchitis, and respiratory allergies follows a consistent trend across age groups and health center locations. The total number of cases for each disease type is highest in the age group of 25-60, emphasizing the vulnerability of individuals within this demographic to respiratory diseases. These findings contribute valuable insights into the complex interplay of age, geographical location, and industrial proximity in influencing respiratory health. The direct correlation between disease prevalence and proximity to cement factories highlights the need for targeted public health interventions and emphasizes the importance of considering both age and environmental factors in health management strategies. This research contributes to the broader understanding of the intricate relationship between industrial activities, age-specific susceptibility, and community health outcomes.

Table 4. Respiratory allergies diseases categorized under different age groups in 2022 of all health centers.

Table 4. Respiratory allergies diseases categorized under different age groups in 2022 of all health centers.

Types of disease	Age groups	Reji H.C	Itaya H.C	Enchini H.C	Olonkom H.C	Karkaresa H.C	Enchini P. Hospital
Bronchial asthma	0 to 5	4	1	2			3
	6 to 24	38	18	27	7	12	30
	25 to 60	274	98	155	43	73	134
	> 61	10	5	11	1	2	7
	Total	326	122	195	51	87	174
Chronic bronchitis	0 to 5	5	2	7		3	5
	6 to 24	254	189	287	58	125	198
	25 to 60	312	165	380	69	113	270
	> 61	9	7	7	5	4	8
	Total	580	363	681	132	245	481
Skin infection	0 to 5	3	2	2	2		6
	6 to 24	176	79	178	23	55	144
	25 to 60	187	89	201	19	67	187
	> 61	3	4	5	1	2	4
	Total	369	174	386	45	124	341
Acute bronchitis	0 to 5	5	3	9	2	4	6
	6 to 24	550	325	652	121	178	598
	25 to60	357	145	454	65	103	389
	> 61	20	15	18	5	10	15
	Total	932	488	1133	193	295	1008
Respiratory allergies	0 to 5	2		1	3	2	4
	6 to 24	116	87	123	40	67	128
	25 to 60	198	109	204	54	97	189
	> 61	5	2	3	2	3	5
	Total	321	198	331	99	169	326

This study primarily centers on five prominent infectious and respiratory diseases: acute bronchitis, bronchial asthma, chronic bronchitis, respiratory allergies, and skin infection. The investigation involved surveying clinical data gathered from Reji, Itaya, and Enchini health centers spanning the years 2015 to 2021. Additionally, data was collected from Karkaresa and Olonkomiii health centers for the period of 2017 to 2021, along with Enchini Primary Hospital data from 2016 to 2021. Acute bronchitis emerged as a significant respiratory ailment affecting these communities, with chronic bronchitis and respiratory allergies following suit. A study by Nkhama et al. (2017) [5] revealed an association between particulate matter (PM) and respiratory symptoms in Chilanga, Zambia.

The study delved into the impact of PM emissions on diseases across age groups, highlighting that individuals aged 25 to 60 were the most significantly affected, closely followed by the age group of 6 to 24. In contrast, the age groups of 0–5 and > 61 were less susceptible to these diseases. Notably, the infant age group (0-5) and the elderly group experienced comparatively lower exposure to PM emission-related diseases, likely due to limited participation in cement factory activities and reduced mobility away from their residences. It is evident that the health of school-age individuals, the middle-aged population, and the active segment of the community residing near the factory are at greater risk of adverse impacts from PM emissions. Similar findings have been reported by other researchers, including Zhang, Jiao et al. (2018) [28] and Beketie, Angessa et al. (2022) [16].

3.5. Exposure differences between cases and controls

To enhance statistical power, a case-to-control ratio of two controls per case was employed, resulting in a sample size of 4768. The distribution across various health centers included 2528 cases and 2240 controls in Reji Health Centre, 2726 cases and 2042 controls in Enchini Health Centre, 2330 cases and 2438 controls in Enchini Primary Hospital, 520 cases and 4248 controls in Olonkomii Health Centre, 920 cases and 3848 controls in Karkaressa Health Centre, and 1345 cases and 3423 controls in Itaya Health Centre, excluding smokers [24]. The formula n = N / (1 + N(e)²) was applied to determine the sample size, with n representing the number of samples, N the total population, and e the margin of error set at 0.05 for a 95% confidence level.

The exposure results among cases and controls, including unexposed cases and controls, are presented in Table 4. The categories denote individuals subject to or free from exposure to cement plant emissions. Each health center's total sample size is indicated, along with calculated risk, odds, relative risk (RR), and odds ratio (OR) values. In Reji Health Centre, with 2528 cases and 2240 controls out of a total sample of 4768, the risk of respiratory ailments was 0.53, indicating a noteworthy association between exposure and outcome. The odds ratio (OR) of 3.23 signifies a substantial likelihood of health issues among the exposed group compared to controls. Similar patterns emerge in Enchini Health Centre, where the risk and odds ratio values are 0.57 and 3.8, respectively, suggesting a significant association.

Contrastingly, Olonkomii Health Centre displays a lower risk of 0.1 and an odds ratio of 0.34, indicating a comparatively lower impact of exposure. Enchini Primary Hospital exhibits a risk of 0.49 and an odds ratio of 2.71, portraying an intermediate level of association between exposure and respiratory outcomes. Karkaresa Health Centre and Itaya Health Centre demonstrate risk values of 0.19 and 0.28, respectively, with odds ratios of 0.69 and 1.11, indicating a varying impact on health outcomes. The Control group, consisting of 1253 cases and 3570 controls out of a total sample of 5012, exhibits a risk of 0.25 and an odds ratio of 0.35.

Table 4. The result of exposure among cases and controls with unexposed cases and unexposed controls.

Table 4. The result of exposure among cases and controls with unexposed cases and unexposed controls.

Name of Health centre	Exposed	Unexposed	Total sample	Risk	Odds	RR	OR
Reji Health centre	2528	2240	4768	0.53	1.13	2.12	3.23
Enchini Health centre	2726	2042	4768	0.57	1.33	2.28	3.8
Olonkomii Health centre	520	4248	4768	0.1	0.12	0.4	0.34
Enchini P. Hospital	2330	2438	4768	0.49	0.95	1.96	2.71
Karkaresa Health centre	920	3848	4768	0.19	0.24	0.76	0.69
Itaya Health centre	1345	3423	4768	0.28	0.39	1.12	1.11
Control	1253	3570	5012	0.25	0.35

RR=Relative Risk, OR=Odds Ratio.

The Relative Risk (RR) and Odds Ratio (OR) values offer insights into the strength of association between exposure to cement plant emissions and the occurrence of respiratory cases across different health centers, providing valuable information for public health and environmental management. The obtained odds ratios signify a substantial risk to workers and communities residing in proximity to cement factories, indicating a significant association with the increased risk of hospital admissions for respiratory cases.

4. Conclusion and Recommendations

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