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IJEMInternational Journal of Environmental Medicine
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16 June 2026

Health Effects on the Population of the Mining Corridor Due to Air Pollutants from Particulate Matter Originating in the Coal Sector the Cesar, La Guajira, and Magdalena 2024–2025

Faculty of Health Sciences, University of Magdalena, Santa Marta 470001, Colombia
Int. J. Environ. Med.2026, 1(2), 9;https://doi.org/10.3390/ijem1020009
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Abstract

The aim of this study was to determine the effects of PM10 and PM2.5 particulate matter pollution from the coal mining sector in the three municipalities of Cesar, La Guajira, and El Magdalena on respiratory morbidity in children under 5 years of age and adults over 60 years of age residing in these municipalities. This descriptive time series study included three municipalities in three departments: Algarrobo, Albania, and La Jagua de Ibirico. The SEVCA (Seasonal Environmental Monitoring System) was used to collect PM10 and PM2.5 pollutants. Data on secondary source air quality (RIPS) were collected from the public health services (ESE) in each municipality. The daily average concentration of μg/m3 was used for the statistical analysis of the pollutants. A time series statistical model was applied to compare the temporal variations in exposure levels and the event itself. The air quality data databases were analyzed using descriptive statistics. A logistic regression model was used to assess the association between pollutants and air quality. To account for the effects of time lags in air quality data, moving averages with lags of 0 to 3 days were used. Statistical analyses were performed using R version 4.5.1. We found daily averages of ARI in children under 5 years of age and adults over 60 years of age in the three municipalities of (1.35) admissions per day. The average daily concentrations of μg/m3 for Algarrobo were (29.79 μg/m3) for PM10 and (12.68 μg/m3) for PM2.5, for Albania (33.49 μg/m3) for PM10 and (13.23 μg/m3) for PM2.5, and for La Jagua (41.42 μg/m3) for PM10 and (15.18 μg/m3) for PM2.5. Significant positive associations greater than 1 were obtained between ARI admissions and PM10 and PM2.5 pollutants, with an RR of 1.105, 1.106, 1.125, 1.124, 1.157, and 1.155 95% CI, when PM10 and PM2.5 increase by 10 μg/m3 and for delays of 1 and 1–3 days. In conclusion, we observed significant positive associations between hospital admissions for ARI in children under 5 years of age and adults over 60 years of age for the three municipalities and the pollutants PM10 and PM2.5, which leads us to conclude that there is an epidemiological association and that the change in μg/m3 levels represents a change in the risk of hospital admission for ARI for children under 5 years of age and older adults in this coal corridor of the Colombian Caribbean.

1. Introduction

The relationship between air pollution and health has been studied worldwide since ancient times. The first studies were based on a series of incidents that occurred in industrialized countries during the first half of the 20th century. The incidents in the Meuse Valley (Belgium) in 1930, in Donora (Pennsylvania, USA) in 1948, in Italy where a gas leak caused a large number of miscarriages and problems of all kinds, the toxic gas leak in Bhopal (India), and the London smog catastrophe in December 1952 were the first historically recognized cases of severe effects associated with increased pollution levels [1,2,3]. It was from this last episode that studies using epidemiological designs began to be conducted [4]. The effect of air pollution on human health has been determined through toxicological and epidemiological studies. For experimental toxicological studies, the dose-response and time-response effects are controlled by the researcher; therefore, they have the inherent limitations of experimental studies for ethical and methodological reasons. While epidemiological studies (cohort and time series) are not controlled by the researcher, they have limitations such as the degree of exposure, which is not the same in all sites of a city; individual variations in exposure due to differences in air pollution in indoor and outdoor spaces; and biological variations of individuals, among other limitations that affect the results [5,6]. This issue has become one of the main concerns in public health due to the high risk of illness and death generated on the population exposed to environmental pollutants above the maximum levels established by the World Health Organization (WHO), especially when it comes to sensitive groups (children and older adults) [7]. In fact, the acute effect on mortality is one of the most studied toxic effects [8,9,10,11]. Estimates by various authors suggested that in 2015, a total of 4.2 million premature deaths occurred due to particulate matter pollution [12,13].
The global literature shows an increase in the prevalence and incidence of asthma and acute respiratory diseases (ARIs), especially in those under 10 years of age, adolescents, and older adults [7,14,15,16,17]. Regarding hospital admissions for ARI, Renzi et al. (2022) [18], in their national study of environmental pollution and respiratory diseases in Italy, mention that 4,154,887 respiratory admissions were recorded during the period 2006–2015, of which 29% were for lower respiratory tract diseases (LRTIs), 12% for COPD, 6% for upper respiratory tract diseases (URTIs), and 3% for asthma. Marchetti et al. (2023) conducted a time series study in Italy to examine the association between long-term exposure to air pollutants and hospital admissions, finding associations between air pollution and suffering from rhinitis and COPD [19].
Mortality associated with particulate matter air pollution has been extensively studied; respiratory infections (RRIs) are among the most common causes of death worldwide, accounting for 2.7 million deaths in 2018 [20]. One of the most studied air pollutants due to its harmful health effects is PM (particulate matter). Air pollution is mostly caused by the combustion of fossil fuels, which releases particulate matter in gaseous form. PM is composed of mixtures of elemental carbon, organic carbon, and compounds such as heavy metals, metal oxides, condensed acids, sulfates, and nitrates, among others. Particulate matter is measured in terms of airborne concentration (μg/m3), where concentration is the mass of PM (µg) per unit volume of air (m3). According to the studies reviewed for this study, health effects begin with the presence of material with an aerodynamic diameter of 10 μg and less. PM10 (particles less than 10 micrometers in diameter) and PM2.5 (particles less than 2.5 micrometers in diameter) are known as respirable particulate matter because these types of PM can penetrate the respiratory system’s defense mechanisms and reach the bronchi or even the pulmonary alveoli [21,22,23]. PM’s effect on human health depends on its size, chemical composition, and microbiological content. Numerous studies over time have demonstrated a significant relationship between chronic exposure to PM and negative health effects [24,25,26,27,28]. Ho et al. (2022) found in their study that high concentrations of PM10 and PM2.5 were associated with an increased risk of emergency department admissions for all causes, cardiovascular disease, and acute respiratory infection [29]. A review of the literature has shown experimental studies that demonstrate that exposure to PM2.5 increases susceptibility to various pathogens, such as bacteria and viruses, in the respiratory system (Yang 2020 [30]). PM2.5 particles penetrate the lungs, irritate and corrode the alveolar walls, and consequently impair lung function [31]. According to the WHO (2021) [7], these particles are the most strongly associated with cardiovascular and respiratory diseases, as well as cancer. In California, between 2006 and 2019, hospital admissions to intensive care units for circulatory and respiratory diseases increased due to PM2.5 exposure [32]. Priyankara et al. (2021) observed that each 10 μg/m3 increase in PM2.5 and PM10 was associated with a 1.95% (0.25, 3.67) increased risk of hospitalization for respiratory illnesses for PM2.5 and a 1.63% (0.16, 3.12) increased risk for PM10 [33]. A study conducted in four cities in Massachusetts, USA, showed that PM2.5 pollution was associated with an increase in cardiovascular and respiratory hospitalizations, particularly in people over 65 years of age [34,35,36,37]. PM10 and PM2.5 were associated with an increased risk of hospitalizations and emergency department visits for COPD exacerbations [38,39,40,41,42], as well as with increased emergency department visits for asthma [43,44,45]. PM2.5 was associated with increased pediatric visits in patients with acute respiratory failure [46,47,48,49,50,51]. Several studies have found significant associations between PM2.5 and cardiovascular and respiratory mortality [52,53]. PM2.5 enters the upper respiratory tract, penetrating the bronchioles and alveoli, and can even enter the bloodstream, causing significant crises in patients (Yang et al., 2020 [30]); (Chalvatzaki et al., 2025 [54]). Cafora et al. (2024) found that PM2.5 was associated with increased levels of inflammatory biomarkers, altering the immune system and inflammatory processes [55]. Kobayashi et al. (2023) observed significant associations between PM2.5 and decreased lung function in an adolescent population [56].
The WHO, in its current guidelines (2021), defines a 24 h daily average of 15 μg/m3 for both PM10 and PM2.5 [7]. In Colombia, the Ministry of Environment, according to Resolution 2254 of 2017, established the maximum permissible daily average values at 70 μg/m3 for PM10 and 37 μg/m3 for PM2.5 [57].
Finally, it is important to note that the study of air pollution and its impact on public health is vital to achieving the Sustainable Development Goals (SDGs). The goals that can be impacted are SDG 3—Good Health and Well-being; SDG 11—Sustainable Cities and Communities; SDG 13—Climate Action; and SDG 7—Affordable and Clean Energy (WHO 2021) [7].
In Colombia, the departments of Magdalena, Cesar, and La Guajira are widely known for their mining activity, especially coal mining, which is one of the main economic activities of these departments. Transnational companies such as Drummond, Glencore, and Anglo American, and national companies such as El Cerrejón, operate in the region, exporting coal to international markets. Coal production in La Guajira and El Cesar has been significant, with more than 1.3 billion tons extracted between 1982 and 2020 (National Mining Agency, 2023) [58]. This open-pit mining activity has led to the destruction of ecosystems, the loss of biodiversity, and water and air pollution. It is estimated that over a 30-year period, a total of 10 streams in La Guajira and 15 in El Cesar have dried up, affecting water availability for local communities. The exposure of the population in this area to water and air pollution has generated health problems. La Guajira is one of the departments in Colombia with very deficient public health conditions; infant mortality is high, with 32.24% of deaths in children under 5 years of age per thousand children for 2015 (National Institute of Public Health (INSP) 2015) [59], and access to basic services such as drinking water and sanitation greatly affects this community.
Based on the above background, this study aims to determine the effects of PM10 and PM2.5 particulate matter pollution from the Carboniferous Sector in three municipalities of Cesar, La Guajira, and El Magdalena on respiratory morbidity in children under 5 years of age and adults over 60 years of age residing in these municipalities in order to develop sustainable and equitable strategies in public health.

2. Methodology

Study type:
Descriptive, observational time series study, describing a specific event in defined populations and areas. These are widely used in studies to evaluate the effects of environmental pollution on health [60]. In a time series design, a population in a given area is defined as a population exposed to a risk. Epidemiologically speaking, this population is a control group that is exposed to several days of exposure to a factor where it is expected that individual variables such as socioeconomic level, diet, and physical activity will change little over the course of several days and thus not be confounding variables [60,61,62,63].

2.1. Population and Study Area

We studied three (3) municipalities in three (3) departments: La Guajira (Albania) (GU), Cesar (La Jagua de Ibirico) (CE), and Magdalena (Algarrobo) (MA). In 2024, the total population of Albania (GU) was 35,875, of which 3250 were under 5 years old and 5252 were over 60 years old. The total population of La Jagua de Ibirico (GU) was 54,368, with 4675 under 5 years old and 7550 over 60 years old. The total population of Algarrobo (MA) was 17,549, with 1509 under 5 years old and 2439 over 60 years old (DANE 2024, Bogota (CO)). In all three municipalities there is a presence of coal mining activity due to the existence of coal mines and railway for transporting the mineral. The 99% of coal-related mining titles in these municipalities correspond to small and medium-scale mining, and only 3% to large-scale mining. Approximately 88% of coal production is extracted from open-pit mining operations (National Mining Agency, 2023, Bogota (CO)) [58].

2.2. Data Collection and Information Sources

For the collection of the pollutants to be studied, PM10 and PM2.5, the National Air Measurement and Surveillance Network (SEVCA, Bogota (CO)) was used, managed by the Regional Autonomous Corporations of each department and by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM, Bogota (CO)); each of these networks in each municipality has a specific number of measurement stations that make up the SEVCA in each municipality. These corporations must conduct daily measurements of pollutants, using 24 h averages, according to the criteria established in Resolution 2254 of 2017, following the established procedures, frequencies, and methodologies. The data generated by these corporations and IDEAM are reported in the Air Quality Information Subsystem (SISAIRE, Bogota (CO)) [64]. We obtained the databases from the three corporations for the years 2020, 2021, 2022, 2023, 2024, and 2025. Working for this study, we used data from 2024 and 2025. The number of stations included in the study were 16 for La Jagua de Ibirico, 9 for Albania, and 2 for Algarrobo. We calculated the average daily PM10 and PM2.5 measurements from all stations for each municipality.
Admission data were collected from secondary sources, Individual Records of Health Service Provision (RIPS, Bogota (CO)) obtained from the three State Social Enterprises (ESE, Bogota (CO)) of each municipality, which included hospital admissions, emergency consultation, and daily medical consultation for respiratory disease, for the infant population (children under 5 years old) and the adult population (over 60 years old). The data were obtained for the period between 2024 and 2025.

2.3. Statistical Analysis

Descriptive analysis:
Initially, the variables (pollutants PM10, PM2.5, and total daily admissions) were subjected to a descriptive procedure using frequency distributions and descriptive statistics of trend and distribution. These were performed using the R 4.5.1 program (R Foundation, Vienna, Austria).
For the pollutants, the daily average of μg/m3 of the total number of stations in the three municipalities was collected. The meteorological data necessary for the modeling, such as wind direction and speed, solar radiation, cloud cover, precipitation, humidity and temperature, were obtained from the meteorological stations of each municipality in the network of the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) [64].
Model for assessing the association between admissions and pollutants:
First, a statistical time series model was applied to compare the temporal variations of exposure levels (PM10 and PM2.5 concentrations) and of the event (hospital admissions for respiratory disease), and to observe changes in the frequency of the disease in the population and study areas. These were performed using the R 4.5.1 program (R Foundation, Vienna, Austria).
Causal inference from this type of time series analysis may be limited due to changes in disease diagnostic criteria and difficulties arising from latency periods between exposure and effects or from the measurement of exposure [65].
For the analysis of the behavior of the series (morbidity data), descriptive methods and time series were used for its prediction, using the ARIMA methodology (Box and Jenkins in 1976) [65], since our data series has a constant mean and variance and a stationary behavior. ARIMA models are based on the theory of stochastic processes; through this we will describe the variability in the diagnoses of daily morbidity by hospital consultations for respiratory diseases, adjusted by periodic trends of 7 days and seasonal trends of 22 weeks by using moving averages of order 7 and thus predict future behaviors. Before adjusting for periodic and seasonal trends, a comparative graph of the climatological variables in each municipality was made, and it was observed that the variation in precipitation was significant, which allowed us to define the period between January and the first two weeks of April as dry months, and the last two weeks of April and the entire month of May as rainy months. All of this was completed using the R 4.5.1 program (R Foundation, Vienna, Austria).
Subsequently, for the analysis of epidemiological association between admissions and contaminants, the ICD 11 diagnoses in the admissions databases were divided into acute and chronic upper and lower respiratory diseases. The group of acute upper and lower respiratory diseases contains diagnoses such as acute upper respiratory infections (J00–J06), acute lower respiratory infections (J20–J22), influenza and pneumonia (J10–J18), chronic respiratory diseases (J40–J47), and other upper respiratory diseases (J30–J39) [66]. These were performed using the R 4.5.1 program (R Foundation, Vienna, Austria).
To evaluate the association between pollutants and admissions, a logistic regression model was performed; the number of daily admissions takes values of zero or positive. It was considered that when the logistic regression coefficient of the variable is positive, we obtain a relative risk (RR) greater than 1 and therefore it corresponds to a risk factor, indicating that there is an epidemiological association, and it represents the change in the risk of hospital admission. Conversely, if it is negative, the RR will be less than 1, indicating that there is no epidemiological association. The relative risk (RR) for analyzing the effect of an air pollutant depends on the level of exposure; therefore, by calculating the RR, the impact on public health of the study populations can be defined when poor air quality is present. To account for the effects of delays in admissions, moving averages of delays (0 to 3 days) were considered. We used the R 4.5.1 program (R Foundation, Vienna, Austria) for this purpose.

2.4. Ethical Considerations

The present study was carried out taking into account the principles contemplated in the Declaration of Helsinki during the 18th World Medical Assembly held in 1964 in the city of Helsinki (Finland), and in accordance with the 2003 amendment of the same declaration, also taking into account what is considered in Resolution 8430 issued by the Ministry of Health (now of Social Protection) of the Republic of Colombia in 1993 for research work, Bogota (CO).

3. Results

Descriptive statistics:
The descriptive analysis of the ARI admission databases of the three (3) municipalities, Albania, La Jagua de Ibirico, and Algarrobo, in both the child and elderly population diagnosed between 2024 and August 2025, yielded a total average of (1.35) daily admissions in children under 5 years of age and adults over 60 years of age in the three municipalities. The proportion of admissions is similar in the three municipalities; the age group of children under 5 years of age was the group that presented the most admissions with (89%) in 2024 and (86%) for January–August 2025 for the municipality of Algarrobo; for Albania (92%) for 2024 and (85%) for 2025; and for La Jagua de Ibirico (92%) in 2024 and (91%) in 2025. (See Table 1 and Table 2.)
Table 1. Frequency distribution of ARI in children under 5 years of age.
Table 2. Descriptive statistics of ARI in children under 5 years of age.
Of the total ARI, upper respiratory tract infections were the diagnostic group with the highest percentage in the under 5 age group with (57%) in 2024 and (77%) in 2025 for Algarrobo; (40%) in 2024 and (38%) in 2025 for Albania; and(42%) in 2024 and (39%) in 2025 for La Jagua de Ibirico. (See Table 3.)
Table 3. Frequency distribution according to ARI diagnosis in children under 5 years of age.
The age group of adults over 60 years old presented percentages of ARI consultations of (8%) in 2024 and (14%) for 2025 for the municipality of Algarrobo; (7.8%) for 2024 and (10%) for 2025 for Albania; and (12%) in 2024 and (10%) in 2025 for La Jagua de Ibirico. (See Table 4.)
Table 4. Frequency distribution of ARI in adults over 60 years of age.
Descriptive statistics for PM10 and PM2.5:
The average daily 24 h concentrations of μg/m3 for PM10 and PM2.5 for the study period in the municipality of Algarrobo were (29.79 μg/m3) for PM10 and (12.68 μg/m3) for PM2.5. In Albania, PM10 levels were 33.49 μg/m3 and PM2.5 levels were 13.23 μg/m3, and in La Jagua, PM10 levels were 41.42 μg/m3 and PM2.5 levels were 15.18 μg/m3. See Table 5 and Table 6 and Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6. The daily averages for both PM10 and PM2.5 in the three municipalities did not exceed the maximum permissible value established by the Ministry of Environment (Resolution 2254 of 2017).
Table 5. Descriptive statistics PM10, 2024–2025.
Table 6. Descriptive statistics PM2.5 2024–2025.
Figure 1. PM10 distribution, 2024−2025, Algarrobo. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Figure 2. PM10 distribution, 2024–2025, Albania. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Figure 3. PM10 distribution, 2024–2025, La Jagua de Ibirico. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Figure 4. PM2.5 distribution, 2024–2025, Algarrobo. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Figure 5. PM2.5 distribution, 2024–2025, Albania. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Figure 6. PM2.5 distribution, 2024–2025, La Jagua de Ibirico. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Statistical model of time series:
For the time series analysis, a descriptive comparative and statistical significance analysis of the meteorological variables was carried out in the first instance, and it was observed that the variation of precipitation was significant, which allowed the period between January and the first two weeks of April to be defined as dry months, and the last two weeks of April and the entire month of May as the rainy period.
The analysis of the stationary trend for diagnostic admissions showed a stationary trend with a small increase in the month of May (weeks 15 to 21). This indicates that to predict its evolution over time it is necessary to deseasonalize it by calculating centered moving averages of order 7 (7 days of the week).
To deseasonalize the data, predict future values, and describe the relationship between the series components, a classic multiplicative model was used. This model was chosen because our series exhibits a highly variable level and its components are interdependent. The selected moving average weights were all points weighted equally by 5 (see Table 7).
Table 7. Description of the classic multiplicative model, 1–7 days a week.
Observing the seasonal factors obtained, Thursday and Tuesday of the seasonal component reach the highest values, while Friday and Saturday take the lowest values (see Table 8).
Table 8. Factors seasonal.
After controlling for seasonality and trends in total admissions, these series did not exhibit any clear stationary cycle, thus precluding any correlation with seasonal meteorological variables. The expected behavior of these series for the coming years and for the same months would show a trend similar to that of the period studied. However, the descriptive analysis of the distribution of admissions, both in the under-5 and over-60 age groups, and for the study period by month and across the three municipalities, showed an increase in the number of admissions from March to May. This coincided with the months of increased rainfall in this Caribbean region. This indicates that precipitation, and in general temperature, humidity, and wind speed, may have an effect on the occurrence of respiratory illnesses. (See Figure 7 and Figure 8.)
Figure 7. Distribution of ARI in children under 5 years of age in the three municipalities, 2024. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Figure 8. Distribution of ARI in children under 5 years of age in the three municipalities, 2025. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.
Model for assessing the association between admissions and pollutants:
The PM10 and PM2.5 pollutant models with delays of (0–3 days) and adjusted for confounding factors showed significant associations between PM10 and PM2.5 (p < 0.05) with ARI admissions for the three municipalities with an increase of 10 μg/m3. For the municipality of Algarrobo, an RR of 1.105 (CI 95% [1.05–1.15]) was found for PM10 and 1.106 (CI 95% [1.05–1.16]) for PM2.5, thus the risk of admission for ARI increases by 10% when PM10 and PM2.5 increase by 10 μg/m3. For Albania, the relative risk (RR) was 1.125 (95% CI [1.07–1.17]) for PM10 and 1.124 (95% CI [1.07–1.16]) for PM2.5, indicating that the risk of admission for acute respiratory infection (ARI) increases by 12% when PM10 and PM2.5 increase by 10 μg/m3. La Jagua had an RR of 1.157 (95% CI [1.09–1.19]) for PM10 and 1.155 (95% CI [1.08–1.19]) for PM2.5; thus, the risk of admission for ARI increases by 15% when PM10 and PM2.5 increase by 10 μg/m3 (see Table 9, Figure 9).
Table 9. RR for PM10 and PM2.5 with an increase of 10 μg/m3 for the three municipalities.
Figure 9. PM10 and PM2.5 associations in the three municipalities, 2025. Source: Author’s own elaboration. Health effects on the population of the mining corridor due to air pollutants from particulate matter originating in the coal sector. Cesar, La Guajira and Magdalena. 2024–2025.

4. Discussion

This study determined the effects of air pollution from particulate matter PM10 and PM2.5 originating from the coal mining corridor in the three municipalities of Cesar, La Guajira, and Magdalena on respiratory morbidity in children under 5 years of age and adults over 60 years of age residing in these municipalities; for this purpose, it evaluated the relationship between hospital admissions for acute respiratory infections (ARIs) and the pollutants PM10 and PM2.5. It is one of the first studies conducted for these departments.
Average daily 24 h concentrations of μg/m3 were found for PM10 of (29.79 μg/m3) and for PM2.5 of (12.68 μg/m3) in Algarrobo, of (33.49 μg/m3) for PM10 and of (13.23 μg/m3) for PM2.5 in Albania, and of (41.42 μg/m3) for PM10 and of (15.18 μg/m3) for PM2.5 in La Jagua. These averages in the three municipalities did not exceed the maximum permissible value established by the Ministry of Environment in Colombia of 75 μg/m3 for PM10 and 37 μg/m3 for PM2.5; however, they did exceed the permissible averages by the WHO (2021) [7] of 50 μg/m3 for PM10 and 25 μg/m3 for PM2.5. The highest averages were found in the municipality of La Jagua. Several authors in their studies in different parts of the world found daily averages of these pollutants that exceed the values permissible by the WHO for their study periods (Croft et al., 2020 [27]); (Tsai et al., 2021 [67]); (Stafoggia et al., 2013 [68]); (Trees et al., 2024 [69]); (Thi et al., 2025 [70]); (Hammer et al., 2020 [71]), (Reid et al., 2021 [72]); (Shin et al., 2021 [48]); (Heo et al., 2022 [44]); (Ho et al., 2022) [29]; (Sang et al., 2022 [73]); (Southerland et al., 2022 [74]); (Wikuats et al., 2023 [75]); (Wu et al., 2023 [76]); (Bi et al., 2023 [17]); (Zhou et al., 2023 [77]); (Kaleta et al., 2023 [78]); (Ruiz et al., 2023 [79]); (Alves et al., 2025 [80]).
Regarding daily admissions for acute respiratory infections (ARIs), it was found that the under-5 age group had the highest number, with averages of 89%, 86%, and 92% of admissions, respectively. Within this group, upper respiratory tract infections were the most common diagnosis, with percentages ranging from 40% to 77%. Sherris et al. (2021) found percentages of 60% in the 1–5 year age group [47], Tsai et al. (2021) found 53% of emergency department visits for pneumonia in children under 5 years of age [67], and Bi et al. (2023) found percentages of 70% of emergency department visits for ARI in children under 5 years of age [17].
We found significant positive associations greater than 1 between ARI admissions and PM10 and PM2.5 pollutants, with RRs of (1.105, 1.106, 1.125, 1.124, 1.157, 1.155 (95% CI)), when PM10 and PM2.5 increased by 10 μg/m3 and for delays of 1 and 1–3 days. Our results agree with those obtained by several authors who described significant associations such as (Wu et al., 2023 [76]) who reported an increase in RR in ARI of 1.56 (CI 0.54–2.58); (Shin et al., 2022 [49]) reported an RR of ARI hospitalization for PM2.5 of 0.90% (CI 0.33–1.41); (Li et al., 2021 [43]) reported increases of 0.46% (CI 0.21–0.70) in admissions; (Ho et al., 2022 [29]) found an RR of 1.025 (95% CI 1.021–1.029); and (Huang et al., 2023 [45]) reported an OR (0.19–0.69%) for PM2,5 at 7.6 μg/m3.
Authors such as (Bi et al., 2023 [17]) obtained an RR of 1.01 (CI 1.00–1.02) for delays of 0–3 days; (Tsai et al., 2021 [67]) reported an OR of 18.2% (CI 8.8–28.4) for PM10 and PM2.5 and pneumonia emergencies in children; (Priyankara et al., 2021 [33]) reported increases of 10 μg/m3 for PM10 and PM2.5 with (CI 0.25–3.67); (Pini et al., 2021 [36]) reported an RR of 1.06 and 1.08 for each increase of 10 in PM10 and PM2.5 with delays of 1 day; (Renzi et al., 2022 [18]) reported that each increase of 10 μg/m3 increases for PM10 (0.92–1.49) and for PM2.5 0.76–1.68; Bi et al., 2023 reported an RR of 1.471 with 1 day delays [17]; (Trees et al., 2024 [69]) reported that every 1 rank increase in exposure is associated with a 0.37% (CI 0.23–0.52) risk of 1 emergency department visit for ARI; Chen et al., 2024 found that increases in PM2.5 increase the use of intensive care for ARI [32]; and finally, Thi et al., 2025 mentions that increases in PM2.5 increase the risk of hospitalization for asthma, with an RR of 1.490 (CI 1.207–1.840) [70].
This type of epidemiological–ecological study has limitations, such as the possibility of falling into the well-known “ecological fallacy,” although in this study, conclusions were not drawn about individuals but about specific population groups. In this study, the possibility of confounding factors is lower than in aggregated geographic studies; in fact, many of the potential confounders can be assumed to be more or less constant over time and, in any case, very poorly correlated with exposure, which was the case in our study. Using time series methods in this study offered an advantage over other epidemiological–ecological designs, as it allowed for the control of time-dependent confounding, including that correlated with exposure, by incorporating trends and seasonality into the causal relationships. Furthermore, by utilizing databases with extensive information, this method increased statistical power and, consequently, enabled the detection of weak associations between response and exposure, such as those found in this study. The descriptive and seasonal analysis of the data allowed for the control of confounding variables that do not affect the estimates of daily pollution variations. Finding that our data series showed no seasonality allowed for greater statistical consistency in the study.

5. Conclusions

This study is the first of its kind in the area known as the “coal corridor” in the Colombian Caribbean and for three municipalities within it: Algarrobo, Albania, and La Jagua. Previous studies had not analyzed the relationship between air pollution and respiratory illnesses; therefore, this study presents current information on the impact of pollution from coal mining activity on the health of children and the elderly in these municipalities.
In that order of ideas, the three municipalities have significant population settlements, whose geographical, climatological, productive, and industrial conditions favor the occurrence of significant levels of air pollution; in the three municipalities for the study period, the daily limit values for PM10 μg/m3 and PM2.5 μg/m3 established by the WHO standard were exceeded; the processes of coal dispersion and open-pit coal mining contribute higher levels of fine particles than coarse particles in these municipalities, resulting in significant pollution levels.
Admissions in the three municipalities and among children under 5 and adults over 60 showed significant proportions for the study period, with upper respiratory tract infections being the most prevalent. An increase in these admissions was observed during the rainy months, suggesting that the variable precipitation and in general temperature, humidity, and wind speed have negative effects on the respiratory tract.
Significant positive associations (RR greater than 1) were observed between admissions for ARI in children under 5 years of age and adults over 60 years of age for the three municipalities and the pollutants PM10 and PM2.5 when PM10 and PM2.5 increased by 10 μg/m3 and for delays of 1 and 1–3 days, which leads us to conclude that there is an epidemiological association and that the change in μg/m3 levels represents a change in the risk of hospital admission for ARI for children under 5 years of age and older adults in this coal corridor of the Colombian Caribbean.
Descriptive epidemiological studies using time series and logistic regression models allow us to conclude with a reasonable degree of certainty that air pollution in municipalities such as Algarrobo, Albania, and La Jagua de Ibirico has negative effects on the health of children and the elderly, leading to an increase in admissions for respiratory illnesses. These types of studies are necessary in departments and regions where they are scarce and data are limited, since having quantitative information makes all the difference in obtaining realistic approximations of the magnitude of the problems in these communities.

Funding

This research received no external funding.

Institutional Review Board Statement

This research is an ecological study that relies solely on secondary databases—national statistics—and therefore does not require authorization from the University's ethics committee.

Data Availability Statement

Access to this data is subject to restrictions. The data were obtained from the institutional information platforms of the competent authorities of both the Ministry of Environment and the General Health System of Colombia.

Conflicts of Interest

The author declares no conflicts of interest.

References

  1. Rosales-Castillo, J.A.; Torres-Meza, V.M.; Olaiz-Fernández, G.; Borja-Aburto, V.H. The acute effects of air pollution on population health: Evidence from epidemiological studies. Public Health 2001, 43, 544–555. Available online: https://saludpublica.mx/index.php/spm (accessed on 1 March 2025).
  2. Bharadwaj, P.; Zivin, J.G.; Mullins, J.T.; Neidell, M. Early-Life Exposure to the Great Smog of 1952 and the Development of Asthma. Am. J. Respir. Crit. Care Med. 2016, 194, 1475–1482. [Google Scholar] [CrossRef] [Scilit] [PubMed] [PubMed Central]
  3. Bell, M.; Davis, D.L.; Fletcher, T. A Retrospective assessment of mortality from the London smog episode of 1952: The role of influenza and pollution. In Urban Ecology; Springer: Boston, MA, USA, 2008. [Google Scholar] [CrossRef] [Scilit]
  4. Wang, G.; Zhang, R.; Gomez, M.; Yang, L.; Zamora, M.L.; Hu, M.; Lin, Y.; Peng, J.; Guo, S.; Meng, J.; et al. Persistent sulfate formation from London Fog to Chinese haze. Proc. Natl. Acad. Sci. USA 2016, 113, 13630–13635. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  5. Hernández-Flórez, L.; Aristizabal-Duque, G.; Quiroz, L.; Medina, K.; Rodríguez, N.; Sarmiento-Suárez, R.; Osorio, G. Air pollution and respiratory illness in children aged less than 5 years-old in Bogotá, 2007. Rev. Salud Pública 2013, 15, 552–565. [Google Scholar]
  6. Ballester, F.; Sáez, M.; Daponte, A.; María Ordóñez, J.; Taracido, M.; Cambra, K.; Arribas, F.; Bellido, J.B.; Guillén, J.J.; Aguinaga, I.; et al. The EMECAS project: Protocol of the multicenter study in Spain of the short-term effects of atmospheric pollution on health. Rev. Esp. De Salud Publica 2005, 79, 229–242. Available online: https://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1135-57272005000200010 (accessed on 1 March 2025). [CrossRef] [Scilit] [PubMed]
  7. World Health Organization. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide; World Health Organization: Geneva, Switzerland, 2021; Available online: http://whqlibdoc.who.int/hq/2006/WHO_SDE_PHE_OEH_06.02_spa.pdf (accessed on 1 March 2025).
  8. Gauderman, W. Air Pollution and Children—An Unhealthy Mix. N. Engl. J. Med. 2006, 355, 78–79. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  9. Schwartz, J. Air Pollution and Daily Mortality: A Review and Meta Analysis. Environ. Res. 1994, 64, 36–52. [Google Scholar] [CrossRef] [Scilit]
  10. Wang, Y.; Shi, Z.; Shen, F.; Sun, J.; Huang, L.; Zhang, H.; Chen, C.; Li, C.; Hu, J. Associations of daily mortality with short-term exposure to PM2.5 and its constituents in Shanghai, China. Chemosphere 2019, 233, 879–887. Available online: https://www.elsevier.com/locate/chemosphere (accessed on 1 March 2025). [CrossRef] [Scilit]
  11. Thomas, J.; Luben, J.; Nichols, S.; Dutton, E.; Owens, E.; Datko, W.; Madden, M.; Sacks, J.D. A systematic review of cardiovascular emergency department visits, hospital admissions and mortality associated with ambient black carbon. Environ. Int. 2017, 107, 154–162. [Google Scholar] [CrossRef] [Scilit]
  12. Pope, C.A., III; Lefler, J.S.; Ezzati, M.; Higbee, J.D.; Marshall, J.D.; Kim, S.Y.; Burnett, R.T. Mortality risk and fine particulate air pollution in a large, representative cohort of US adults. Environ. Health Perspect. 2019, 127, 77007. [Google Scholar] [CrossRef] [Scilit]
  13. Health Effects Institute. State of Global Air 2017; Special Report; Health Effects Institute: Boston, MA, USA, 2017; Available online: https://www.stateofglobalair.org/sites/default/files/soga_2017_report.pdf (accessed on 1 March 2025).
  14. Butt, E.W.; Turnock, S.T.; Rigby, R.; Reddington, C.L.; Yoshioka, M.; Johnson, J.S.; Regayre, L.A.; Pringle, K.J.; Mann, G.W.; Spracklen, D.V. Global and regional trends in particulate air pollution and the attributable burden on health over the past 50 years. Environ. Res. Lett. 2017, 12, 104017. [Google Scholar] [CrossRef] [Scilit]
  15. CDC. Case Studies in Environmental Medicine (CSEM), Environmental Triggers of Asthma, General Information About Asthma. 2009. Available online: https://archive.cdc.gov/www_atsdr_cdc_gov/es/csem/asma/asma_descripcion.html (accessed on 1 March 2025).
  16. Fang, L.; Gao, P.; Bao, H.; Tang, X.; Wang, B.; Feng, B.; Cong, S.; Juan, J.; Fan, J.; Lu, K.; et al. Chronic obstructive pulmonary disease in China: A nationwide prevalence study. Lancet Respir. Med. 2018, 6, 421–430. Available online: https://www.frontiersin.org/ (accessed on 2 March 2025). [CrossRef] [Scilit]
  17. Bi, J.; D’Souza, R.; Moss, S.; Senthilkumar, N.; Russell, A.; Scovronick, C.; Chang, H.; Ebelt, S. Acute Effects of Ambient Air Pollution on Asthma Emergency Department Visits in Ten U.S. States. Environ. Health Perspect. 2023, 131, 47003. [Google Scholar] [CrossRef] [Scilit]
  18. Renzi, M.; Scortichini, M.; Forastiere, F.; de’ Donato, F.; Michelozzi, P.; Davoli, M.; Gariazzo, C.; Viegi, G.; Stafoggia, M.; Ancona, C.; et al. A nationwide study of air pollution from particulate matter and daily hospitalizations for respiratory diseases in Italy. Sci. Total Environ. 2022, 807, 151034. [Google Scholar] [CrossRef] [Scilit]
  19. Marchetti, P.; Miotti, J.; Locatelli, F.; Antonicelli, L.; Baldacci, S.; Battaglia, S.; Bono, R.; Corsico, A.; Gariazzo, C.; Maio, S.; et al. Long-term residential exposure to air pollution and risk of chronic respiratory diseases in Italy: The BIGEPI study. Sci. Total Environ. 2023, 884, 163802. [Google Scholar] [CrossRef] [Scilit]
  20. Domingo, J.; Rovira, J. Effects of air pollutants on the transmission and severity of respiratory viral infections. Environ. Res. 2020, 187, 109650. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  21. Harrison, R.M.; Yin, J. Particulate matter in the atmosphere: Which particle properties are important for its effects on health. Sci. Total Environ. 2000, 249, 85–101. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  22. Sarnat, J.A.; Brown, W.K. Ambient Gas Concentration and Personal Particulate Matter Exposures. Epidemiology 2005, 16, 385–395. [Google Scholar] [CrossRef] [Scilit]
  23. Ballester, F. The assessment of the health impact of air pollution. Rev. Environ. Health 2003, 3, 102–107. Available online: https://dialnet.unirioja.es/servlet/articulo?codigo=5785104 (accessed on 1 March 2025).
  24. Gauderman, W.J.; Urman, R.; Avol, E.; Berhane, K.; McConnell, R.; Rappaport, E.; Chang, R.; Lurmann, F.; Gilliland, F. Association of improved air quality with lung development in children. N. Engl. J. Med. 2015, 372, 905–913. [Google Scholar] [CrossRef] [Scilit]
  25. Raaschou, O.; Andersen, Z.J.; Beelen, R.; Samoli, E.; Stafoggia, M.; Weinmayr, G.; Hoffmann, B.; Fischer, P.; Nieuwenhuijsen, M.J.; Brunekreef, B.; et al. Air pollution and lung cancer incidence in 17 European cohorts: Prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). Lancet Oncol. 2017, 14, 813–822. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  26. Cao, T.; Thompson, J.E. Fully portable personal monitoring of PM2.5 for health and exposure studies. Anal. Lett. 2017, 50, 712–723. [Google Scholar] [CrossRef] [Scilit]
  27. Croft, D.P.; Zhang, W.; Lin, S.; Thurston, S.W.; Hopke, P.K.; Van Wijngaarden, E.; Rich, D.Q. Associations between source-specific particulate matter and respiratory infections in New York state adults. Environ. Sci. Technol. 2020, 54, 975–984. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  28. Chai, G.; He, H.; Sha, H.; Zhai, G.; Zong, S. Effect of PM2.5 on daily outpatient visits for respiratory diseases in Lanzhou, China. Sci. Total Environ. 2019, 649, 1563–1572. [Google Scholar] [CrossRef] [Scilit]
  29. Ho, A.F.W.; Hu, Z.; Woo, T.Z.C.; Tan, K.B.K.; Lim, J.H.; Woo, M.; Liu, N.; Morgan, G.G.; Ong, M.E.H.; Aik, J. Ambient air quality and emergency hospital admissions in Singapore: A time series analysis. Int. J. Environ. Res. Public Health 2022, 19, 13336. [Google Scholar] [CrossRef] [Scilit]
  30. Yang, L.; Li, C.; Tang, X. The impact of PM2.5 on the host defense of respiratory system. Front. Cell Dev. Biol. 2020, 8, 91. [Google Scholar] [CrossRef] [Scilit]
  31. Xing, Y.F.; Xu, Y.H.; Shi, M.H.; Lian, Y.X. The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 2016, 8, E69–E74. Available online: https://jtd.amegroups.org/article/view/6353/6196 (accessed on 1 March 2025).
  32. Chen, E.; Teyton, A.; Benmarhnia, T. The temporal trend and disparity in short-term health impacts of fine particulate matter in California (2006–2019). Sci. Total Environ. 2024, 954, 176543. [Google Scholar] [CrossRef] [Scilit]
  33. Priyankara, S.; Senarathna, M.; Jayaratne, R.; Morawska, L.; Abeysundara, S.; Weerasooriya, R.; Bowatte, G. Ambient PM2.5 and PM10 exposure and respiratory disease hospitalization in Kandy, Sri Lanka. Int. J. Environ. Res. Public Health 2021, 18, 9617. Available online: https://www.mdpi.com/1660-4601/18/18/9617 (accessed on 2 March 2025). [CrossRef] [Scilit] [PubMed]
  34. Zheng, X.-Y.; Ding, H.; Jiang, L.-N.; Chen, S.-W.; Zheng, J.-P.; Qiu, M.; Zhou, Y.-X.; Chen, Q.; Guan, W.-J. Association Between Air Pollutants and Asthma Emergency Room Visits and Hospital Admissions in Time Series Studies: A Systematic Review and Meta-Analysis. PLoS ONE 2015, 10, e0138146. [Google Scholar] [CrossRef] [Scilit]
  35. Bell, M.; Ebisu, K.; Leaderer, B.; Gent, J.; Lee, H.; Koutrakis, P.; Wang, Y.; Dominici, F.; Peng, R. Associations of PM2.5 Constituents and Sources with Hospital Admissions: Analysis of Four Counties in Connecticut and Massachusetts (USA) for Persons ≥ 65 Years of Age. Environ. Health Perspect. 2013, 122, 138–144. Available online: https://pmc.ncbi.nlm.nih.gov/articles/PMC3915260/ (accessed on 1 March 2025). [CrossRef] [Scilit]
  36. Pini, L.; Giordani, J.; Gardini, G.; Concoreggi, C.; Pini, A.; Perger, E.; Vizzardi, E.; Di Bona, D.; Cappelli, C.; Ciarfaglia, M.; et al. Emergency department admission and hospitalization for COPD exacerbation and particulate matter short-term exposure in Brescia, a highly polluted town in northern Italy. Respir. Med. 2021, 179, 106334. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  37. Chen, C.; Chan, A.; Dominici, F.; Peng, R.D.; Sabath, B.; Di, Q.; Bell, M.L. Do temporal trends of associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalizations differ by sub-populations and urbanicity—A study of 968 US counties and the Medicare population. Environ. Res. 2022, 206, 112271. [Google Scholar] [CrossRef] [Scilit]
  38. Liu, J.C.; Wilson, A.; Mickley, L.J.; Dominici, F.; Ebisu, K.; Wang, Y.; Bell, M.L. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology 2017, 28, 77–85. [Google Scholar] [CrossRef] [Scilit]
  39. Xu, Q.; Li, X.; Wang, S.; Wang, C.; Huang, F.; Gao, Q.; Wu, L.; Tao, L.; Guo, J.; Wang, W.; et al. Fine Particulate Air Pollution and Hospital Emergency Room Visits for Respiratory Disease in Urban Areas in Beijing, China, in 2013. PLoS ONE 2016, 11, e0153099. [Google Scholar] [CrossRef] [Scilit]
  40. Altzibar, J.M.; Tamayo-Uria, I.; De Castro, V.; Aginagalde, X.; Albizu, M.V.; Lertxundi, A.; Dorronsoro, M. Epidemiology of asthma exacerbations and their relation with environmental factors in the Basque Country. Clin. Exp. Allergy 2015, 45, 1099–1108. [Google Scholar] [CrossRef] [Scilit]
  41. Tian, Y.; Xiang, X.; Juan, J.; Sun, K.; Song, J.; Cao, Y.; Hu, Y. Fine particulate air pollution and hospital visits for asthma in Beijing, China. Environ. Pollut. 2017, 230, 227–233. [Google Scholar] [CrossRef] [Scilit]
  42. Maio, S.; Fasola, S.; Marcon, A.; Angino, A.; Baldacci, S.; Bilò, M.; Bono, R.; La Grutta, S.; Marchetti, P.; Sarno, G.; et al. Relationship of long-term air pollution exposure with asthma and rhinitis in Italy: An innovative multipollutant approach. Environ. Res. 2023, 224, 115455. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  43. Li, Y.; Li, C.; Liu, Y.; Meng, C.; Xu, C.; Liu, Z.; Wang, Q.; Liu, Y.; Han, J.; Xu, D. An association between PM2.5 and pediatric respiratory outpatient visits in four Chinese cities. Chemosphere 2021, 280, 130843. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  44. Heo, S.; Son, J.Y.; Lim, C.C.; Fong, K.C.; Choi, H.M.; Hernandez-Ramirez, R.U.; Nyhan, K.; Dhillon, P.K.; Kapoor, S.; Prabhakaran, D.; et al. Effect modification by sex for associations of fine particulate matter (PM2.5) with cardiovascular mortality, hospitalization, and emergency room visits: Systematic review and meta-analysis. Environ. Res. Lett. 2022, 17, 53006. [Google Scholar] [CrossRef] [Scilit]
  45. Huang, G.; Brown, P.; Shin, H.H. Multi-pollutant case-crossover models of all-cause and cause-specific mortality and hospital admissions by age group in 47 Canadian cities. Environ. Res. 2023, 225, 115598. [Google Scholar] [CrossRef] [Scilit]
  46. Maciejewska, K. Short-term impact of PM2. 5, PM10, and PMc on mortality and morbidity in the agglomeration of Warsaw, Poland. Air Qual. Atmos. Health 2020, 13, 659–672. [Google Scholar] [CrossRef] [Scilit]
  47. Sherris, A.; Begum, B.; Baiocchi, M.; Brooks, W.; Luby, S. Associations between ambient fine particulate matter and child respiratory infection: The role of particulate matter source composition in Dhaka, Bangladesh. Environ. Pollut. 2021, 290, 118073. [Google Scholar] [CrossRef] [Scilit]
  48. Shin, H.H.; Gogna, P.; Maquiling, A.; Parajuli, R.P.; Haque, L.; Burr, B. Comparison of hospitalization and mortality associated with short-term exposure to ambient ozone and PM2.5 in Canada. Chemosphere 2021, 265, 128683. [Google Scholar] [CrossRef] [Scilit]
  49. Shin, H.H.; Maquiling, A.; Thomson, E.M.; Park, I.W.; Stieb, D.M.; Dehghani, P. Sex-difference in air pollution-related acute circulatory and respiratory mortality and hospitalization. Sci. Total Environ. 2022, 806, 150515. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  50. Yang, X.; Zhang, Y.; Zhan, X.; Xu, X.; Li, S.; Xu, X.; Chen, Z. Particulate matter exposure is highly correlated to pediatric asthma exacerbation. Aging 2021, 13, 17818. [Google Scholar] [CrossRef] [Scilit]
  51. Zafirah, Y.; Lin, Y.K.; Andhikaputra, G.; Deng, L.W.; Sung, F.C.; Wang, Y.-C. Mortality and morbidity of asthma and chronic obstructive pulmonary disease associated with ambient environment in metropolitans in Taiwan. PLoS ONE 2021, 16, e0253814. [Google Scholar] [CrossRef] [Scilit]
  52. Atkinson, R.W.; Kang, S.; Anderson, H.R.; Mills, I.C.; Walton, H.A. Epidemiological time series studies of PM2.5 and daily mortality and hospital admissions: A systematic review and meta-analysis. Thorax 2014, 69, 660–665. Available online: http://thorax.bmj.com/content/69/7/660.full.pdf (accessed on 2 March 2025). [CrossRef] [Scilit]
  53. Atkinson, R.W.; Mills, I.C.; Walton, H.A.; Anderson, H.R. Componentes de partículas finas y salud: Una revisión sistemática y metaanálisis de estudios epidemiológicos de series temporales de mortalidad diaria e ingresos hospitalarios. J. Expo. Sci. Environ. Epidemiol. 2015, 25, 208–214. Available online: https://doi-org.biblioteca.unimagdalena.edu.co/10.1038/jes.2014.63 (accessed on 2 March 2025). [CrossRef] [Scilit]
  54. Chalvatzaki, E.; Chatoutsidou, S.; Diapouli, L.; Gini, M.; Manousakas, M.; Samoli, E.; Eleftheriadis, E.; Lazaridis, M. Dosimetry simulations of ultrafine particles deposition to the human respiratory tract and transport to the olfactory region for female receptors. Atmos. Environ. 2025, 348, 121103. [Google Scholar] [CrossRef] [Scilit]
  55. Cafora, M.; Rovelli, S.; Cattaneo, A.; Pistocchi, A.; Ferrari, L. Short-term exposure to fine particulate matter exposure impairs innate immune and inflammatory responses to a pathogen stimulus: A functional study in the zebrafish model. Environ. Pollut. 2024, 348, 123–841. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  56. Kobayashi, S.; Yoda, Y.; Takagi, Y.; Ito, Y.; Wakamatsu, J.; Nakatsubo, R.; Horie, Y.; Hiraki, T.; Shima, M. Short-term effects of the chemical components of fine particulate matter on pulmonary function: A repeated panel study among adolescents. Sci. Total Environ. 2023, 895, 165–195. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  57. Ministry of Environment, Government of Colombia. Resolution 2254 of November 2017. 2017. Available online: https://www.minambiente.gov.co/wp-content/uploads/2021/10/Resolucion-2254-de-2017.pdf (accessed on 2 March 2025).
  58. National Mining Agency. Coal Chapter. Technical Books. 2023. Available online: https://www.anm.gov.co/ (accessed on 2 March 2025).
  59. National Institute of Public Health (INSP). Event Report: Integrated Surveillance of Death in Children Under Five Years of Age Due to Acute Respiratory Infection, Acute Diarrheal Disease, or Malnutrition in Colombia. 2015. Available online: https://www.ins.gov.co/Paginas/Inicio.aspx (accessed on 2 March 2025).
  60. Gasparrini, A.; Armstrong, B.; Kenward, M.G. Nonlinear distributed delay models. Stat. Med. 2010, 29, 2224–2234. [Google Scholar] [CrossRef] [Scilit]
  61. Bhaskaran, K.; Gasparrini, A.; Hajat, S.; Smeeth, L.; Armstrong, B. Time series regression studies in environmental epidemiology. Int. J. Epidemiol. 2013, 42, 1187–1195. [Google Scholar] [CrossRef] [Scilit]
  62. Flanders, W.D.; Klein, M.; Darrow, L.A.; Strickland, M.J.; Sarnat, S.E.; Sarnat, J.A.; Waller, L.A.; Winquist, A.; Tolbert, P.E. A method for detecting residual confounding factors in time series and other observational studies. Epidemiología 2011, 22, 59–67. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  63. Brumback, B.A.; Ryan, L.M.; Schwartz, J.D.; Neas, L.M.; Stark, P.C.; Burge, H.A. Transitional regression models, with application to environmental time series. J. Am. Stat. Assoc. 2000, 95, 16–27. [Google Scholar] [CrossRef]
  64. IDEAM. Institute of Hydrology, Meteorology and Environmental Studies. Consultation and Download of Meteorological Data. 2025. Available online: https://www.ideam.gov.co/atencion-y-servicios-a-la-ciudadania (accessed on 2 March 2025).
  65. Stellwagen, E.; Tashman, L. ARIMA: The Models of Box and Jenkins. Foresight Int. J. Appl. Forecast. 2013, 29, 28–33. Available online: https://www.researchgate.net/publication/285902264_ARIMA_The_Models_of_Box_and_Jenkins/citation/download (accessed on 2 March 2025).
  66. International Classification of Diseases for Morbidity and Mortality Statistics. ICD-11.OMS. 2023. Available online: https://www.minsalud.gov.co (accessed on 1 March 2025).
  67. Tsai, M.-T.; Ho, Y.-N.; Chiang, C.-Y.; Chuang, P.-C.; Pan, H.-Y.; Chiu, I.-M.; Tsai, C.-M.Y.; Cheng, F.-J. Effects of fine particulate matter and its components on emergency department visits for pediatric pneumonia: A time-stratified crossover case study. Int. J. Environ. Res. Public Health 2021, 18, 10599. [Google Scholar] [CrossRef] [Scilit]
  68. Stafoggia, M.; Samoli, E.; Alessandrini, E.; Cadum, E.; Ostro, B.; Berti, G.; Med-Particles Study Group. Short-term associations between fine and coarse particulate matter and hospitalizations in Southern Europe: Results from the MED-PARTICLES project. Environ. Health Perspect. 2013, 121, 1026–1033. Available online: https://pmc.ncbi.nlm.nih.gov/articles/PMC3764077/ (accessed on 2 March 2025). [CrossRef] [Scilit]
  69. Trees, I.; Yu, F.; Deng, X.; Luo, G.; Zhang, W.; Lin, S. Ultrafine Particles and Hospital Visits for Chronic Lower Respiratory Diseases in New York State. Ann. Am. Thorac. Soc. 2024, 21, 1147–1155. Available online: https://pubmed.ncbi.nlm.nih.gov/38445971/ (accessed on 2 March 2025). [CrossRef] [Scilit]
  70. Thi, H.; Ramachandran, S.; Nguyen, D.; Roper, C. Investigating the acute effects of black carbon, PM2.5 exposure, and temperature on asthma and respiratory-related emergency department visits and hospitalizations in Mississippi. Environ. Pollut. 2025, 373, 126150. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  71. Hammer, M.S.; Van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A.M.; Hsu, N.C.; Martin, R.V. Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ. Sci. Technol. 2020, 54, 7879–7890. [Google Scholar] [CrossRef] [Scilit]
  72. Reid, C.; Considine, E.; Maestas, M. Daily estimates of PM2.5 concentration by county, zip code, and census tract in 11 western states, 2008–2018. Sci. Data 2021, 8, 12–25. Available online: https://doi-org.biblioteca.unimagdalena.edu.co/10.1038/s41597-021-00891-1 (accessed on 2 March 2025). [CrossRef] [Scilit]
  73. Sang, S.; Chu, C.; Zhang, T.; Chen, H.; Yang, X. The global burden of disease attributable to ambient fine particulate matter in 204 countries and territories, 1990–2019: A systematic analysis of the Global Burden of Disease Study 2019. Ecotoxicol. Environ. Saf. 2022, 238, 113588. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  74. Southerland, V.; Brauer, M.; Mohegh, A.; Hammer, M.S.; Van Donkelaar, A.; Martin, R.V.; Anenberg, S.C. Global urban temporal trends in fine particulate matter (PM2.5) and attributable health burdens: Estimates from global datasets. Lancet Planet. Health 2022, 6, e139–e146. [Google Scholar] [CrossRef] [Scilit]
  75. Wikuats, C.F.H.; Nogueira, T.; Squizzato, R.; de Freitas, E.D.; Andrade, M.d.F. Health risk assessment of exposure to air pollutants exceeding the new WHO air quality guidelines (AQG) in São Paulo, Brazil. Int. J. Environ. Res. Public Health 2023, 20, 5707. [Google Scholar] [CrossRef] [Scilit]
  76. Wu, Z.; Liang, F.; Chen, X.; Liu, G.; Li, G.; Tian, L.; Guo, Q.; Yang, G.; Zhou, Z.; Pan, X.; et al. Hospital admission risks and ambient fine particulate matter exposure in Beijing, China. Atmos. Environ. 2023, 288, 119291. [Google Scholar] [CrossRef] [Scilit]
  77. Zhou, J.; Gladson, L.; Díaz Suárez, V.; Cromar, K. Respiratory Health Impacts of Outdoor Air Pollution and the Efficacy of Local Risk Communication in Quito, Ecuador. Int. J. Environ. Res. Public Health 2023, 20, 6326. [Google Scholar] [CrossRef] [Scilit]
  78. Kaleta, D.; Kozielska, B. Spatial and temporal volatility of PM2.5, PM10 and PM10-linked B[a]P concentrations, and assessment of the exposure of the Silesian population in 2018–2021. Int. J. Environ. Res. Public Health 2023, 20, 138. [Google Scholar] [CrossRef] [Scilit]
  79. Ruiz, R.; Díaz, J.; López, J.; Asensio, C.; Ascaso, M.; Saez, M.; Luna, M.; Barceló, M.; Navas, M.; Linares, C. Short-term effects of air pollution and noise on emergency hospital admissions in Madrid and economic assessment. Environ. Res. 2023, 219, 115147. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  80. Alves, C.A.; Sanchez de la Campa, A.; Cipoli, Y.; Furst, L.; Higawa, G.; Leitão, A.; da Silva, A.V.; Feliciano, M.S. PM10-bound elements in Luanda’s urban atmosphere: Concentrations, sources, and their environmental and health impacts. Environ. Pollut. 2025, 372, 125995. Available online: https://www.sciencedirect.com/science/article/pii/S0269749125003689 (accessed on 2 March 2025). [CrossRef] [Scilit] [PubMed]
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