
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12315-8
10.1016/j.heliyon.2024.e36284
e36284
Research Article
Airborne particulate matter integral assessment in Magdalena department, Colombia: Patterns, health impact, and policy management
Vergara-Vásquez Eliana evergarav@unimagdalena.edu.co
ab⁎⁎
Hernández Beleño Luis M. ab
Castrillo-Borja Tailin T. ab
Bolaño-Ortíz Tomás R. c
Camargo-Caicedo Yiniva ab
Vélez-Pereira Andrés M. avelezp@academicos.uta.cl
de⁎
a Programa de Ingeniería Ambiental y Sanitaria, Facultad de Ingeniería, Universidad del Magdalena, Santa Marta, Colombia
b Grupo de Investigación en Modelación de Sistemas Ambientales (GIMSA), Facultad de Ingeniería, Universidad del Magdalena, Santa Marta, Colombia
c School of Natural Resources Engineering, Department of Agricultural Science, Universidad Católica del Maule, Curicó, Chile
d Departamento de Ingeniería Mecánica, Facultad de Ingeniería, Universidad de Tarapacá, Arica, Chile
e Laboratorio de Investigaciones Medioambientales de Zonas Áridas, Universidad de Tarapacá, Arica, Chile
⁎ Corresponding author. Departamento de Ingeniería Mecánica, Universidad de Tarapacá, Av. 18 de Septiembre 2222, Arica, Chile. Tel.: +56582207338. avelezp@academicos.uta.cl
⁎⁎ Corresponding author. Grupo de Investigación en Modelación de Sistemas Ambientales (GIMSA), Santa Marta, Colombia. Tel.: +573024203779. evergarav@unimagdalena.edu.co
15 8 2024
30 8 2024
15 8 2024
10 16 e3628414 5 2024
20 7 2024
13 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 μg/m3, and the minimum was 3 μg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

Graphical abstract

Image 1

Highlights

• Coal industry explains PM levels in specific periods under study.

• New PM sources should be considered due to socioeconomic dynamics.

• The increasing PM trend in results is crucial due to progressive restrictive laws.

• First health risk assessment by PM exposition in Colombian Caribbean.

• Current law management process could be insufficient to avoid PM pollution.

Keywords

Air pollution assessment
Cancer risk
Coal ports
Non-parametric test
PM10
Temporal trends
==== Body
pmc1 Introduction

Urban air pollution is among the most significant environmental issues facing developing and transitional economies [1]. Studies show that approximately 100 million people in Latin America and the Caribbean are exposed to unhealthy air quality, which surpasses the guidelines established by the World Health Organization (WHO). In 92 out of 122 cities in the WHO dataset, concentration of particulate matter with an aerodynamic diameter of less than 10 μm (PM10) exceeded the annual guideline [2]. Consequently, many countries and non-governmental organizations have set up air quality monitoring stations or networks [3,4] with specialized instruments [5].

Particulate matter (PM), especially PM10, receives the most attention among the monitored criteria pollutants [6] because the relationship between exposure to this pollutant and morbidity and mortality is widely acknowledged [7,8]. PM is related to 2.9 million deaths annually worldwide [9], where the size and chemical composition determines its harmful health effects [10].

Scientific studies suggest links between PM and several health problems, including asthma [11,12], bronchitis [13], cardiovascular diseases [14], lung cancer [15,16], acute and chronic respiratory symptoms [17], among others. Also, studies have shown that exposure to particulate matter is related to emergency admissions for pneumonia [18] and an increased risk of acute respiratory infection (ARI) symptoms [19]. In Colombia, ARI morbidity accounts for around 5 % of all outpatient visits and 7 % of all hospitalizations [20].

According to WHO recommendations, the annual and 24-h concentrations of PM10 should not exceed 20 and 50 μg/m3, respectively [21]. Many countries have established specific air quality standards [22]. This decision is mainly justified by considering in the dynamics of the economic growth of the communities, taking into account the implications of (i) increased morbidity and mortality [2], (ii) higher urbanization and vehicle usage due to urban development [23,24], and (iii) the competitive economic costs associated with implementing of air quality policies [25,26]. For instance, in China, economic development has been prioritized over environmental pollution control for decades [27]. The country's industrialization and urbanization have led to increased energy resource consumption, putting more pressure on air pollution prevention and control [28]. Air quality has been a concern in Russia for many years due to environmental issues stemming from centralized planning during the Soviet period, which emphasized heavy industrialization and dense resettlement in newly built production cities [29].

Regulatory standards require continuous monitoring of air quality through air quality monitoring systems (AQMS) and long-term data analysis. The derived results allow evaluation of the behavior of air quality pollution and the influence of the proposed mitigation strategies [30]. Several international studies have assessed long-term PM data to determine the spatiotemporal variability of PM in different lands used [31], to identify temporal trends or interannual behavior [32], and to compare with regulations or international standards [4,33].

In Colombia, there is a lack of time series studies to assess the impact of air pollution. Blanco-Becerra et al. [34] conducted a study on the effect of PM10 on mortality in Bogotá from 1998 to 2006. They found that increased PM10 concentrations are linked to acute mortality from respiratory causes. However, in the Colombian Caribbean region, there are only short-term studies related to total suspended particles (TSP) or PM10 in Barranquilla [35,36] and Riohacha [[37], [38], [39]], which resulted in the identification of potential sources of PM10. Magdalena's department has also contributed to this trend [40]. The most extensive research analyzed TSP over four years from 2006 to 2009, revealing higher TSP concentrations in the last two years surveyed [41]. Likewise, concerning levels were found between February 2004 and March 2005 [42].

More recently, two studies concluded that PM10 levels exceeded the international precautionary levels of 50 μg/m3 in Santa Marta [43,44]. Finally, the 2021 air quality report published by Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM) stated that Colombia's cities, including Santa Marta, exhibited higher incidences of PM10 and PM2.5 (particulate matter with less than 2.5 μm aerodynamic diameter), which according to the Air Quality Index (IQA), are responsible for health risks and conditions [45].

When it comes to managing air pollution internationally, the focus is on reducing emissions from vehicles and industry, improving the quality of traditional fuels (fossils/biomass), introducing new sustainable fuels, and promoting cleaner production processes [46,47]. In Colombia, air quality management guidelines have been established under CONPES 3943/2018, which sets out management guidelines for implementing air quality policies and standards. Unfortunately, the strategic lines of this policy only focus on reducing fixed and mobile emission sources, highlighting the need to create control strategies to prevent and reduce these emissions [48]. This has left local environmental authorities with insufficient resources to implement effective management actions [49].

The Magdalena department in Colombia has recognized the importance of addressing climate change. They have established a policy for 2040 with the aims of promoting renewable energies, improving energy efficiency, and protecting local ecosystems. This policy also includes adaptation strategies for vulnerable sectors such as agriculture and coastal infrastructure, with the goal of achieving effective long-term climate change mitigation and adaptation results [50]. However, this policy does not include any measures to control the PM.

The study area is known by the gathering, loading, and export of coal in maritime ports, which marks the end of the production process that starts in the Cesar and La Guajira departments (also in the Caribbean region), the largest mining areas in the country adjacent to the area under study. This economic activity is associated with PM fugitive emissions [42], particularly during coal transport, loading, and unloading. This was reinforced by the issuance of Decree 3083 of 2007, which mandated seaports to implement direct loading to minimize fugitive emissions [51]. Additionally, since 1999, the Magdalena department has had an Air Quality Monitoring System (SVCA) managed by the Corporación Autónoma Regional del Magdalena – CORPAMAG [52]. However, historical atmospheric PM data over the long-term have not yet been assessed. This article aimed to comprehensively evaluate the long-term PM concentration in the Magdalena department in the Colombian Caribbean. The concentration will be analyzed to i) study the spatiotemporal concentration patterns, ii) examine the relationship with meteorological conditions, iii) analyze temporal trends, iv) assess potential health impacts, and v) evaluate the effect of management measures on long-term concentrations.

2 Materials and methods

2.1 Study area

Magdalena department is located between latitudes 11°36′58″ N and 8°56′25″ N and between longitudes 73°32′50″ W and 74°56′45″ W. It is situated in the natural regions of the Sierra Nevada de Santa Marta and the lower part of the Magdalena River basin, also known as the Lower Magdalena Valley. The department covers an area of 23,188 km2, which represents 2.03 % of the national territory and 11.5 % of the Caribbean Plain [53]. The southern part of the department experiences temperatures above 28 °C, while the center and northern parts oscillate between 26 and 28 °C. On average, annual precipitation in most of the department ranges between 50 and 100 rainy days per year. This number may be lower in isolated sectors of the coastal strip. Towards the foothills of the Sierra Nevada, the number of rainy days increases to more than 200 days at medium altitudes [54]. Magdalena's economy centers on agriculture, livestock, tourism, and port activities.

On the other hand, the activities linked to the last two sectors are considered the primary sources of PM emissions in the area (Fig. 1). The ports, such as Puerto Santa Marta, Prodeco (a subsidiary of Glencore), Drummond, and Colombian Natural Resources, export approximately 30 million tons of coal annually and contribute significantly to air pollution [55]. Additionally, the tourism industry brings in numerous tourists, increasing the number of visitors, hence the increase in vehicles and flights, at least four times a year. Furthermore, the vehicle fleet has doubled in less than five years. Although the Magdalena department is not heavily industrialized, there are potential sources of PM emissions from industrial and natural sources such as marine aerosols and wildfires [56].Fig. 1 Study area with details of SVCA of Magdalena department.

Fig. 1

2.2 Database

For this work, it was used data retrieved from three data bases: PM10 and PM2.5 concentrations were taken from IDEAM's Air Quality Information Subsystem – SISAIRE (http://sisaire.ideam.gov.co/ideam-sisaire-web/consultas.xhtml). Daily concentration data of PM10 were collected from 2002 to 2021 for 15 stations, while PM2.5 was collected from 2017 to 2021 for three stations. The Magdalena department Air Quality System has eleven active stations for PM10, three of which also measure PM2.5. The details are shown in Table 1 and Fig. 1. The data was manipulated according to the “Protocolo para el monitoreo y seguimiento de la calidad del aire, made by the Minambiente, Colombia”; a year was considered valid as long as 75 % of the data was available. All analyses were performed with the statistical programming software R (see script into supplementary material).Table 1 Summary of air quality station and meteorological in Magdalena department.

Table 1
Station	Coordinates	Technology	Characterization of station	Meteorological station	
Y	X	Z (masl)	Type and Environment	Period	% Data	Years included	
Aeropuerto	11°07′16.3″N	74°13′53.3″W	6	Manual	Transport - suburban	2017–2021	87.6	2	Arpto. Simón Bolívar	
Alcatraces	11°05′08.9″N	74°13′02.8″W	30	Manual	Coal industry - suburban	2003–2021	88.6	17	Arpto. Simón Bolívar	
Carbograneles	11°06′38.4″N	74°13′44.2″W	NA	Manual	Transport - suburban	2007–2012	91.5	6	Arpto. Simón Bolívar	
Ciénaga Koica a	11°00′38.6″N	74°14′38.0″W	8	Automatic	Transport - urban	2021–2021	97.3 (97.3 %)	1	Ciénaga Koica	
Cordobita	11°01′26.9″N	74°12′11.8″W	96	Manual	Coal industry - rural	2017–2019	90.6	3	Costa Verde	
Costa Verde	11°01′19.0″N	74°14′47.0″W	6	Manual	Coal industry - rural	2009–2021	84.5	9	Costa Verde	
Don Jaca	11°05′54.7″N	74°13′07.6″W	22	Manual	Coal industry - suburban	2009–2021	88.3	12	Arpto. Simón Bolívar	
Jolonura	11°02′46.9″N	74°11′42.11″W	79	Manual	Coal industry - rural	2017–2021	86.0	3	Costa Verde	
Marina Santa Martab	11°14′25.1″N	74°13′00.1″W	5	Manual	Coal industry - urban	2008–2021	89.0	13	Corpamag	
Molinos Santa Marta	11°11′40.2″N	74°11′36.1″W	20	Manual	Industry - urban	2012–2017	89.1	6	Corpamag	
Pescaito	11°14′59.6″N	74°12′24.8″W	17	Manual	Coal industry - urban	2016–2021	83.7	4	Corpamag	
Playitas	11°02′49.3″N	74°13′53.9″W	3	Manual	Coal industry - suburban	2019–2021	93.0	2	Costa Verde	
Tayrona Koica a	11°16′35.9″N	74°07′00.9″W	62	Automatic	Background - rural	2018–2018	78.9 (0.0 %)	1	Tayrona Koica	
Unimag Koica a	11°13′23.0″N	74°11′08.8″W	20	Automatic	Transport - urban	2018–2021	89.2 (89.3 %)	3	Unimag Koica	
Zuana	74°13′30.5″N	11°08′26.37″W	NA	Manual	Transport - suburban	2008–2014	89.6	5	Arpto. Simón Bolívar	
a Report PM2.5 data are availed in parenthesis.

b This station had a minor location change in 2016.

The meteorological data was provided by the IDEAM, its repository DHIME contains all the meteorological information used in this project (http://dhime.ideam.gov.co/webgis/home/). Such data is from six stations. Three of these were associated with Koica stations (air pollutant stations). The other air quality stations were paired with the nearest meteorological station (Table 1). Lastly, health information was retrieved from Sistema Nacional de Vigilancia en Salud Pública (SIVIGILA). Such repository belongs to the Instituto Nacional de Salud de Colombia – INS (https://portalsivigila.ins.gov.co/Paginas/Buscador.aspx). This entity reports morbidity due to ARI (International code if ARI: 995) per epidemiological week. The classification comprises emergency + outpatient consultation, hospitalization, and intensive care in the two cities covered by SVCA (Santa Marta and Ciénaga). The datasets are available from 2016 to 2021.

2.3 Data analysis

Valid data were plotted in an annual series to compare their concentration and patterns. We used descriptive statistics to summarize and compare national and international standards, check Scripts S1 to S6 from supplementary material. Spearman-Rho and Mann-Kendall tests were used to correlate meteorological data and PM10 concentration and test temporal trends' statistical significance [57], check Script S7 and Script S8 from supplementary material for more details about the process. These tests do not require data adjustment to normal distribution assumptions [58]. Also tests are often double feature in long-term data due to their unbiasedness and consistency [59]; while the magnitude of the trend was estimated with the Theil-Sen's slope [57], given that its applications allow a median-based approach, which offers less sensitivity to outliers and gross errors, in comparison to the classical least squares regression based on a weighted average [60].

Additionally, a five-year rolling trend was developed to determine the effects of management-cause change in the coal industry, legislative process, or any other change in the dynamics of an area under study. The data were previously checked using Shapiro's test, which shows a lack of normality in the annual series data.

The study also analyzed the potential effects on health in two aspects. Firstly, we used Bortman's methodology to make an endemic channel, so we could turn IRA's data into a dichotomous variable and make a logistic regression model for each pollutant station and estimate the odd ratios between morbidity due to ARI and PM concentrations [61], check Script S9 for more details. Secondly, the risk of cancer associated with PM [62], check Script S10. The odd ratios are showed by the city under study and type of ARI morbidity (emergency + outpatient consultation, hospitalization, and ICU) for an increase of 10 μg/m3 of PM10. This information was later compared with Theil Sen's global trends for the PM10 as future scenarios.

In the case of cancer risk, the methodology proposed by the EPA and recently employed by Santiago et al. [62] was used. Equation (1) shows how to estimate the risk, and Equation (2) shows chronic daily intake. The details of the variables used in the equation are in Table 2.Equation 1 CR=CDI*RR

Equation 2 CDI=CA*IR*ED*EF*LBW*ATL*NY

Table 2 Variables and references values used in the calculation of carcinogenic risk.

Table 2Variable	Value	Unit	Reference	
Chronic daily intake (CDI)	–	mg/kg-d	[62].	
Relative risk (RR)	PM10	General risk	1.08	–	([61,63+]).	
Ex-smoker	1.11	
Smoker	1.18+	
PM2.5	General risk	1.09	
Ex-smoker	1.44	
Smoker	1.06	
Contaminant concentration (CA)	–	mg/m3	SISAIRE-IDEAM	
Inhalation rate (adult) (IR)	1.02	m3/h	[62].	
Exposure duration (adult) (ED)	16.5	h/week	Estimate from Osorio Hernandez and Freile Lopesierra [64],a	
Exposure frequency (EF)	52	Week/yr	[62].	
Exposure lifetime (L)	Male	65.8	yr	Estimate between 8 years old and Life expectancy [65]	
Female	72.1	
Body weight (BW)	Male	68.6	kg	[[66], [67], [68]]	
Female	61.2	
Average life expectancy (ATL)	Male	73.8	yr	[65]	
Female	80.1	
Number of days of years (NY)	365	d/yr		
a Mean bus waiting time (BWT): 15 min, Mean bus track time (BTT): 30 min. The mean number of buses taken daily (NBD) is four trips from Monday to Friday and two trips for Saturday. There are no considered buses on Sundays.

The Hazard Risk (HR) was calculated as the ratio between the Chronic daily intake (CDI) and a reference dose (RfD), which varies depending on the pollutant being studied. For PM10, the RfD value used is 1.1 × 10−2 mg/kg-d [69]. If this ratio exceeds 1, the associated CDI is considered dangerous [62].

To evaluate air quality management strategies, the trends of pollutants monitored by the SVCA were compared. The measures taken by the environmental authorities were retrieved from official documents available on the internet and supplemented by interviews with a professional from CORPAMAG. Additionally, we considered socioeconomic changes in the population and the number of vehicles over time for the trends’ analysis.

3 Results

The Aeropuerto station has reported the highest PM10 concentration, followed by Molinos Santa Marta and Playitas stations (Fig. 2). These stations also have higher exceedances of standards and maximum mean values. However, the absolute maximum values show that the Molinos, Jolonura, and Costa Verde stations also have high values. Comparing the absolute maximum data with quartile 75 (Q75) suggests that these concentrations could be attributed to isolated events, which is supported by the low coefficient of variation observed in all stations except in Ciénaga Koica, which reported close values of Q75 and absolute maximum (Table 3). The absolute maximum is observed between January and April, and sometimes late between May and June. The stations with the lowest annual minimum mean are Jolonura and Playitas, while Tayrona Koica is the background station with the lowest mean annual (Table 3). All annual mean concentrations generally exceeded the WHO standard, while Molinos Santa Marta, Playitas, Aeropuerto, and Jolonura exceeded the national standard. Concerning PM2.5, the first records show that the value is close to the national standard and exceeds the WHO recommendation. In 2021, most values are lower than 50 μg/m3, but there are some high concentrations in the time series and historical distributions.Fig. 2 Annual time series plot per station at Magdalena department with probability density function of PM10 and PM2.5 concentrations. Red line national standard, blue line OMS standard. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Table 3 Summary of PM10 and PM2.5 data on the air quality in the Magdalena department.

Table 3	Absolute maximum		Daily standard exceedance	Annual standard exceedance	
	Station	Minimum ±SD	Mean ± SD	Maximum ±SD	Peak date	Q75	CV	National	WHO	National	WHO	
PM10	Aeropuerto	11.7 ± 7.9	53.1 ± 27.3	143.1 ± 40.7	171.9	03/29/2021	62.3	0.5	16.5 %	52.8 %	50.0 %	100.0 %	
Alcatraces	9.2 ± 3.6	29.2 ± 14.7	73.9 ± 27.1	156.0	04/16/2021	35.5	0.5	1.3 %	12.2 %	0.0 %	100.0 %	
Carbograneles	9.2 ± 3.3	29.3 ± 17.9	78.1 ± 23.4	113.9	01/27/2007	36.0	0.6	3.9 %	11.5 %	0.0 %	100.0 %	
Ciénaga Koica	16.4 ± 0.0	45.1 ± 14.2	89.9 ± 0.0	89.9	01/31/2021	56.3	0.3	1.4 %	45.4 %	0.0 %	100.0 %	
Cordobita	6.0 ± 1.0	30.4 ± 21.0	94.8 ± 33.9	125.7	12/29/2019	35.8	0.7	7.0 %	18.2 %	0.0 %	100.0 %	
Costa Verde	12.9 ± 3.7	38.7 ± 18.6	97.0 ± 35.7	177.1	04/30/2019	48.5	0.5	4.5 %	31.5 %	0.0 %	100.0 %	
Don Jaca	12.3 ± 2.9	35.4 ± 17.7	95.8 ± 31.6	153.5	04/25/2021	43.5	0.5	2.5 %	22.6 %	0.0 %	100.0 %	
Jolonura	5.7 ± 1.8	38.9 ± 28.8	140.8 ± 64.4	186.5	07/26/2019	50.3	0.7	10.3 %	30.8 %	33.3 %	100.0 %	
Marina Santa Marta	9.9 ± 3.5	32.6 ± 15.7	81.9 ± 23.7	132.4	11/26/2015	39.6	0.5	2.2 %	17.2 %	0.0 %	100.0 %	
Molinos Santa Marta	14.6 ± 4.3	51.8 ± 30.0	157.4 ± 38.8	194.5	07/07/2014	64.8	0.6	17.9 %	47.0 %	66.7 %	100.0 %	
Pescaito	8.9 ± 2.0	35.5 ± 14.8	86.9 ± 18.0	110.2	01/13/2016	43.7	0.4	2.0 %	22.7 %	0.0 %	100.0 %	
Playitas	5.8 ± 0.6	45.5 ± 28.0	141.5 ± 4.7	144.8	05/28/2021	62.1	0.6	16.9 %	42.2 %	50.0 %	100.0 %	
Tayrona Koica	5.8 ± 0.0	22.2 ± 10.5	73.9 ± 0.0	73.9	06/09/2018	26.8	0.5	0.0 %	4.2 %	0.0 %	100.0 %	
Unimag Koica	11.6 ± 0.6	35.9 ± 12.0	88.2 ± 12.3	102.4	06/08/2018	42.8	0.3	0.8 %	20.1 %	0.0 %	100.0 %	
Zuana	11.1 ± 3.6	29.0 ± 11.8	65.4 ± 20.1	96.7	02/10/2014	34.9	0.4	0.9 %	7.4 %	0.0 %	100.0 %	
PM2.5	Ciénaga Koica	4.8 ± 0.0	16.9 ± 6.0	43.1 ± 0.0	43.1	02/14/2021	20.3	0.4	0.3 %	57.8 %	0.0 %	100.0 %	
Unimag Koica	4.1 ± 1.0	12.4 ± 5.0	35.6 ± 9.5	44.5	03/19/2019	15.1	0.4	0.1 %	25.8 %	0.0 %	100.0 %	

Fig. 2 describes the time series and frequency distribution per station and PM type. The stations close to transportation sources show the lowest variability, except for Aeropuerto and Carbograneles stations. In contrast, stations near the coal industry report high variability in concentration. Finally, the station close to the industry shows a higher annual mean and an irregular distribution because of its high values. Only Ciénaga and Unimag Koica stations at least 75 % of data in a one-year annual series in PM2.5. Both areas are associated with transport sources, and Ciénaga Koica has higher annual average concentrations.

Analyzing the closest stations to each other (Fig. 1) and their behavior over the years (Table 2), Jolonura and Cordobita stations expose similar values during their common monitoring years, with higher values in the last years of monitoring. Similar results are shown in Ciénaga Koica and Costa Verde stations. Additionally, the concentration distribution frequencies (Fig. 2, right side) indicate that stations with high annual averages also show values in the high classes of the distribution curve. However, the fact stations with a few annual series, such as Aeropuerto, Jolonura, Playitas, display similar patterns. This is concerning, as it generates uncertainty regarding the probability of having high concentrations for a more extended period. The correlation between.

PM and meteorological parameters was only calculated for PM10 based on the available data. Most correlation coefficients fall between −0.5 and 0.5, with precipitation at the Cordobita station slightly exceeding 0.5 (0.516 rounded to three digits) (see Fig. 3). Only two mobile atmospheric pressure (closely related to the atmospheric water content) display an indirect relationship with PM10. Meanwhile, temperature, wind speed, and solar radiation have a positive influence on PM10 concentration. However, wind speed and atmospheric pressure have low statistical significance, while the others exhibit statistical significance in most stations. Additionally, there are opposite relationships: in Costa Verde (wind speed and minimum temperature) statistical significance in the Mann-Kendall and Spearman-Rho tests in Marina Santa Marta.Fig. 3 Correlation coefficients of PM10 and meteorological parameters per Magdalena Department Air quality stations.

WSP: Wind speed. TM: Mean temperature. RH: Relative humidity. PRC: Precipitation. TMIM. Minimum temperature. TMAX: Maximum temperature. APR: Atmospheric pressure. *P-Value <0.05; **P-Value <0.01.

Fig. 3

Fig. 4 shows the trends' results. The test was carried out in only six of 15 stations due to limited data availability. Overall, the concentration of PM10 shows an upward trend in all stations, with values ranging from 0.06 to 8.21 μg/m3-yr. The Alcatraces station has the highest overall trend at 8.21 μg/m3-yr, followed by Molinos Santa Marta at 3.20 μg/m3-yr, and Costa Verde at 1.61 μg/m3-yr. The results indicate that industrial coal stations have steeply increasing trends, while vehicular stations exhibit the lowest increases. However, the behavior of temporal trends per station type is uncertain, mainly due to the lack of continuous annual data for the stations and the absence of statistical significance in global temporal trends. The five-year forward trends show four directional changes. The first period showed decreasing trends from 2003 to 2007, followed by increasing trends until 2011, then decreasing trends until 2014, and finally, increasing trends. On the other hand, a high mobile trend is observed 2010–2014 (5.96 μg/m3-yr in Costa Verde), followed by two subsequent periods (2011–2015: 5.39 μg/m3-yr in Marina Santa Marta; 2011–2015: 4.96 μg/m3-yr in Don Jaca). While these stations do not have the highest concentrations, the trends in the results may indicate a minimal progressive increase over the years.Fig. 4 Global and five-year mobile temporal trends of the concentration of PM10 in Magdalena department.

Fig. 4

The study on the impact of PM10 on the incidence of respiratory diseases (IRA) has revealed that only the air quality stations located in Ciénaga have a statistically significant effect on the number of emergencies + outgoing patient (2.31–3.22), as well as on hospitalization (2.13–6.01) (Table 4). An increase of 10 μg/m3 of PM10 can result in a two-to eight-fold increase in emergency and outpatient patient consultations, and a five-to six-fold increase in hospitalizations for ARI in the municipality of Ciénaga. Among the stations, Alcatraces, Costa Verde, and Jolonura showed a low mean annual concentration but also exhibited high global increasing trends. The Alcatraces station had the highest magnitude of impact. The results also demonstrated that the study area has cancer risk values above 10−4, higher for women than men, particularly in the communities near the Aeropuerto, Molinos Santa Marta, and Playitas stations. Based on the results, it can be inferred that there is a risk probability of 17–20 possible cases for every 104 inhabitants exposed to 1 μg/m3. Consequently, the risk is highest for smokers, followed by ex-smokers and the general population.Table 4 Odd ratios of PM10 versus type of medical consultation of ARI.

Table 4Station	Type of medical consultation for IRA	
Emergency + outgoing patient	Hospitalization	UCI	Total ARI case	
Aeropuerto (SMR)				1.35 [0.51–3.60]	
Alcatraces (CIE)	3.22 [1.28–8.08]	6.01 [1.97–18.38]	0.46 [0.20–1.10]	1.51 [0.42–5.51]	
Carbograneles (SMR)				1.97 [0.42–9.22]	
Ciénaga Koica (CIE)	2.31 [1.27–4.19]	2.13 [1.15–3.94]	0.58 [0.34–0.99]	1.49 [0.66–3.33]	
Cordobita (CIE)	1.23 [0.57–2.64]	1.02 [0.45–2.31]	0.37 [0.15–0.88]	0.70 [0.24–2.04]	
Costa Verde (CIE)	2.86 [1.25–6.54]	5.45 [1.83–16.18]	0.65 [0.32–1.34]	0.99 [0.36–2.72]	
Don Jaca (SMR)				2.56 [0.31–21.06]	
Jolonura (CIE)	1.77 [1.03–3.05]	5.77 [1.96–16.96]	0.87 [0.52–1.43]	1.13 [0.53–2.43]	
Marina Santa Marta (SMR)				0.36 [0.01–24.4]	
Molinos Santa Marta (SMR)				2.20 [0.69–6.98]	
Pescaito (SMR)				0.92 [0.09–9.22]	
Playitas (SMR)	0.80 [0.53–1.23]	0.81 [0.51–1.28]	1.26 [0.82–1.92]	0.69 [0.39–1.22]	
Tayrona Koica (SMR)				1.40 [0.35–5.52]	
Unimag Koica (SMR)				1.37 [0.21–9.17]	
Zuana (SMR)				4.48 [0.34–58.92]	
SMR: refer station in Santa Marta city, and CIE to Ciénaga city.

(Fig. 5). Conversely, the hazard values indicated that exposure to PM10 concentration is unlikely to cause adverse health effects. However, the chemistry composition of PM in the study area must be considered in to be more accurate in estimating their potential hazardous impact.Fig. 5 Box Plot shows adults' cancer risk by PM10 exposition in Magdalena department.

Fig. 5

Particulate matter air pollution management strategies began with Constitutional Court Ruling Nº. SU442 of 1997, which established the presumption that coal transportation, storage, and maritime export activities significantly impact PM concentrations. It prompted the creation of the first SVCA in 1999, with eight stations for TSP (total suspended particles) and three for PM10 (Table 5). The coal industry sector supports the monitoring network, but it is managed by CORPAMAG (the local environmental authority). The primary goal of the SVCA was to collect information about air quality during coal transport to the ports. In 2003, 91 MTon of coal were transported by trucks and 5000 Ton by train and then stacked in the open air and loaded onto open planks towards the ships [70].Table 5 Regulatory advances.

Table 5Regulations and othersa	Actions	
Constitutional Court Ruling Nº. SU442 of 1997	Established that coal activities significantly impact PM levels.	
1999	First SVCA [71]	
Resolution 5369 of 2007	Declared an area of 800 m public interest for port purposes.	
Decree 3083 of 2007	Ports must use enclosed conveyor belts near beaches to load coal without anchoring.	
Decree 4286 of 2009	Ports are required to have a calendar for the implementation of the measures of Decree 3083.	
Resolution 610 of 2010	Air Quality Standard or Immission Level.	
2015	Increased the installed capacity of the SVCA [71]	
Resolution 2254 of 2017	Modify the resolution 610 of 2010, more restrictive values	
CONPES 3943 in 2018	Policy for improving air quality.	
National air quality strategy [72]	National Air Quality Strategies document	
a For more information regarding regulatory, socioeconomic and monitoring regulations, check Fig. S1 in the supplementary material.

The findings of the initial SVCA indicated the necessity for implementing control measures in the coal transportation and loading processes. These measures were outlined in Decree 3083 of 2007, with a three-year implementation period. The standard mandated that companies use enclosed conveyor belts or equivalent systems for direct loading onto vessels. It also regulated aspects such as the height of the storage piles, material humidification systems, and controls for unloading at the port entry. Decree 4286 of 2009 required ports to establish a schedule for implementing these measures in line with the initial deadline. Additionally, Resolution 5369 of 2007 designated an 800-m beach area in Ciénaga, Department of Magdalena, as a public interest zone for port purposes (DPN, 2008). This designation aimed to minimize the direct impact of fugitive emissions associated with economic activity and create spaces for new infrastructure compliant with the requirements of Decree 3083/2007.

The Ministry of the Environment also formulates strategies for good environmental practices through specific environmental guidelines, which are not legally binding. The first guideline, the Environmental Guide for Coal Transportation in 2004, encouraged road and railway transportation companies to modify loading and unloading processes, packaging, and transportation control measures to minimize fugitive emissions. Many of these measures were adopted by the economic sector. The second guideline, the Carboniferous Ports Environmental Guide published in 2005, provided suggestions like those in the Transport Coal Guide. However, environmental management practices became legally binding with the enforcement of Decree 3083 of 2007. In 2015, CORPAMAG increased the installed capacity of the SVCA with 15 stations: nine PST and six PM10. Later, in 2016 three automatic Koica stations (all air criteria pollutants included) were installed, and all TSP stations were removed or changed to PM10 for a total of 15 stations. This decision was deemed necessary due to the population increase, road infrastructure development with the Ruta del Sol I, II and III projects, and the vehicle fleet, which numbered 53,533 vehicles in 2015.

4 Discussion

According to the most recent air quality report in Colombia, there has seen an increase in PM10 concentration. Over 90 % of the stations comply with the annual report, but they still need to report their daily compliance. The study area has shown good results compared to national information. The Magdalena department ranks 8th in PM10 and 9th in PM2.5 according to the annual mean, and this is below the national standard. Nevertheless, the trend test results show increased concentrations, which are above the WHO standard.

The sources described in this research are recognized contributors to air pollution in other studies [42,73,74]. The contribution of each source is influenced by various factors, which could be seasonal or related to a specific event. Stations with the highest concentration per type of environment were Molinos Santa Marta, which is close to industrial sources; Aeropuerto, which is related to transportation; and Playitas, which is linked to the coal industry. Like other stations near coal ports, the Playitas station shows a predominance of PM10 over PM2.5, which is typical for this type of emission sources [75]. Nevertheless, it has the lowest concentration compared to other studies [75,76]. A study in the Tarragona port (Spain) has shown the impact of port activities on PM10 levels due to fugitive mineral emissions, with a contribution of 62 %, mainly related to traffic (34 %), and a concentration of 3 μg/m3 above the urban background area [77]. Similar situation is reported in Ventilla-Callao, where low concentrations were also reported, but the authors note that the lead content is high [78], which will be an issue of interest even in low PM concentrations [79].

Additionally, Manjare et al. [76] demonstrated that wind speed (>8 m/s) increases the concentration of PM10 and PM2.5 above standard within a 1-km radius from the load point in Mormugao port (India), causing emissions to be higher compared to other sources, because coarse PM concentrations increase by resuspension of dust under strong winds [80]. In any case, the type of material, the operation condition (especially loading/unloading), and the weather interact to generate suspended dust particles in the atmosphere. Regarding the Molinos Santa Marta station, CORPAMAG reported that an unpaved heavy-traffic road near the monitoring station increased the levels of PM10, which led to the relocation of this station in 2018.

The influence of meteorological variables on the PM concentration pattern has been exposed. Precipitation and relative humidity in the atmosphere are linked to a scavenging process with suspended particles [[81], [82], [83], [84]], where the magnitude of the relationship depends on the initial concentration of PM10. The net effect of precipitation on the fine and coarse particulate matter concentration strongly depends on the atmospheric stability. During the night and before mid-morning, the atmosphere tends to be stable. A precipitation event currently induces debris collection below the clouds, which reduces the concentration of particles in the atmosphere. During the afternoon, unstable atmospheric conditions predominate, and aerosols disperse vertically. In this period, a precipitation event stabilizes the atmosphere, generating early stabilization that, together with continuous anthropogenic emissions during the day, leads to the accumulation of pollutants near the surface, compensating for the washing effect of precipitation [85]. Moreover, when air viscosity increases, relative humidity increases and particles absorb part of it, significantly affecting their size and leading to an increase in the particle deposition rate [86] Higher PM10 concentrations will be removed more efficiently [87].

Increasing temperature helps reduce PM concentration in the air [88]. After 8:00 a.m., the temperature increases, relative humidity decreases, and the atmospheric convection movement gradually strengthens. The diffusion conditions improve, and particulate mass concentrations gradually decrease, reaching the lowest point in the late afternoon [89]. However, this may vary. Choi and Choi [82] showed that in high concentrations of PM10 in a sandstorm, the temperature tends to be inverse, but for lower concentrations, it is direct. Other studies show that the influence of temperature could vary depending on the PM sources. For instance, PM10 from burning has a direct relationship with temperature [81]. However, the time resolution used influences the correlation between these variables [90], which could explain the results that reveal Jolonura and Costa Verde stations display a different correlation with temperature. Finally, the coefficients of correlation of wind speed show a positive relationship with PM10 (Fig. 3), congruent with other results. They explained the increase in concentration as resuspension, where wind speeds are associated with aerodynamical size. The wind direction is related to the possible source(s), especially when the predominant direction aligns the station and the emission source(s) [81,83]. Vengoechea et al. [91] confirmed the effect of wind, finding that the marine aerosol represents around 20–27 % of the PM10 measured in coastal zones. This explains the dates with maximum values of PM in this study area (Table 3), which are congruent with high wind speed by trade winds between June and April in the study [92]. Table S1 from supplementary material compares our PM and correlations result to other studies [[93], [94], [95], [96], [97]].

The trends shown in scientific literature vary from those present here due to the differing socio-economic and meteorological contexts of different countries. For instance, a study conducted in Shimla, India analyzed the trend of PM10 between 2011 and 2017. The study noted a rising trend in PM10 levels during this period; however, statistical significance was not observed. The authors attribute this outcome to potential influences from weather conditions and a general escalation in vehicular traffic [98]. Similarly, a study in Zabol and Sistan, Iran, observed a statistically significant increasing trend related to wind speed between 2012 and 2020 [99]. These results are closely related to the present findings and explain the pattern of the pollutant in terms of the vehicular fleet and the resuspension of the particles by the winds. This may also explain why concentration tends to continue increasing, despite measures to control fugitive emissions from coal transportation and loading.

The study suggests that the behavior of moving trends in PM concentration can be explained by several factors. The coal industry in the area was possibly responsible for the increasing trends of PM until Decree 3083 in 2007, which changed the transport mode of coal (changing truck transport by train) and loads mode of coal in ports (direct loading system), reducing the fugitive emission of PM. The last increasing trend is attributed to the socioeconomic growth of cities, increasing the population, and hence, the number of vehicles. Additionally, Santa Marta has a high quantity of fine material (462 mg/m2) on the street surface [100], adding another potential PM source to vehicular combustion, industry, and marine aerosols identified in the study area. The reported differences between monitoring and background stations (at least 10 μg/m3) support these assumptions.

Regarding PM2.5, the study identified two air quality monitoring stations reporting data associated with vehicular traffic, confirming the influence of the dominant source [101] and the effect of fugitive emissions due to sediment on the roads [102]. It is worth mentioning that the recorded values are below local standards but exceed the recommendations set by the WHO. These values would be concerning, especially considering that the local maximum permissible values are becoming more restrictive over time and are expected to align with WHO standard. To better understand PM patterns, a comprehensive historical dataset covering the study area is crucial.

Exposure to particulate matter (classified into PM10, PM2.5, and PM1) increases the likelihood of developing an influenza-like illness, as indicated by Lu et al. [103]. Furthermore, COVID-19 patients with respiratory comorbidities face a higher risk of hospitalization when exposed to PM10 [104], with an increase of 5 μg/m3 associated with a 3.1 % higher likelihood of developing ARI [105]. Another study observed that a 10 μg/m3 increment is associated with a 5–7% higher probability of developing ARIs. These findings are particularly significant considering that current trends show PM concentration are between 1 and 8 μg/m3 per year. A study conducted in Bogotá, Colombia, showed that the higher concentrations of PM10 are related to an increase in consultations for ARIs in hospital centers [106]. This study also found that an increase of 10 μg/m3 is associated with an increment in the probability of general consultations for ARIs by around five times. However, the same author mentioned that comparing PM10 to general consultations for ARIs may not show a positive correlation.

The results also suggest that emergency cases could rise by one to four times with a 10 μg/m3 increase in PM10 over two years. However, it is worth noting that the ARI case data is based on cities rather than specific localities, making it difficult to accurately estimate associated risks for the population. Specific data for Santa Marta based on the types of ARI consultations were not included in this analysis. Cancer risk values observed are higher than the threshold set by the EPA (1 × 10−4 mg/kg-d) for the study area, resulting in a high cancer risk per 10,000 inhabitants. However, it is crucial to evaluate the level of danger based on the chemical composition rather than the concentration, especially considering the chemical composition of coal reported by Silva et al. [36]. Now, when the future scenarios as described are analyzed by the trend analysis, it is likely that the results on the risk of cancer and associated danger with exposure to PM10-PM2.5 could worsen. This is endorsed by the possibility of exceeding the current standards recommended by the WHO.

It is well known that there are multiple factors that influence the development of the ARI, either directly or through PM10. These factors include previous illnesses, age, sex, and ethnicity. However, the way the data is provided does not allow us to take into consideration other parameters for a deeper epidemiological analysis [19].

Colombia has made significant progress in managing air quality since the enactment of CONPES 3943 in 2018 at the national and transversal scale. Monitoring reports through 2020 show compliance with 20 % of the identified actions out of a total of 28. Approximately $9,330,000 was spent, with a focus on developing a strategy to reduce vehicle emissions [107]. The annual goal saw a 52.72 % progress, and the final goal saw a 40.96 % progress of Colombia's air quality policy. An investment of $6,422,400 was made for the National Air Quality Strategy -ENCA [108]. Despite these advances, challenges remain, such as the lack of approval of strategies for zero-emission technologies and the adoption of measures to reduce vehicle emissions. This emphasizes the need for continued commitment to address Colombia's air quality challenges [109]. These statements are of interest in the local context and explain why, despite controlling fugitive emissions from the coal industry, the concentration of pollutants tends to increase, possibly due to vehicular traffic.

The implementation of control measures at ports has been a slow process since the release of Decree 3083 in 2007, with processes beginning three years later. Between 2013 and 2014, non-compliance with the standard's requirements was discovered. Similarly, from 2013 to mid-2016, an increase in pollution levels was observed at the Don Jaca station near three coal ports, attributed to coal transportation activities. By the end of 2016, around 2600 tractor-trailers were utilizing the Troncal del Caribe highway near the station (Arévalo, 2019). Beginning in 2014, there was a noticeable rise in the concentration of PM10 concentration at the Marina Santa Marta station, influenced by port activity and the growing population of the city. Santa Marta had less than 500,000 inhabitants in 2013 and had 521,239 inhabitants in 2019 [65], which could lead to an increase in the use of both public and private vehicles, with a fleet of 72,531 vehicles in 2017 (Alcaldía, 2022).

Due to the evolution of the multiple sources that generate particulate matter, the importance of the installation of the Koica automatic systems and the increase in the number of non-automatic monitoring systems in 2015 is justified. This demonstrates progress in management from a technical point of view and compliance with the provisions of CONPES 3943 “Policy for the Improvement of Air Quality”. However, it is necessary not only to report concentration data in the SVCA but also provide chemical and mineralogical characterization to identify the major sources contributing to the air quality in the department. This approach allows for more focused monitoring and prioritization of resources to prevent, mitigate or correct the impacts on air quality in compliance with the National Air Quality Strategies document [72]. It is also necessary to invest in infrastructure to reach those areas where the emission of particulate matter is unknown, due to budget constraints for such projects.

Analyzing the regulatory advances, regarding compliance with the measures generated throughout these years, it is evident that from the imposition of measures on mining activity such as: carry out direct loading (vessels) using encapsulated conveyor belts or other equivalent systems, management of the height of storage piles, application of material humidification systems, controls in the unloading processes upon entry to the port, sheltering of trucks and trains among others, a decrease in the concentration of particulate matter was evident. However, these actions can be overshadowed today by the increase in population and the number of vehicles. It is evident that although Colombia has reduced the permissible parameters through those established in Resolution 2254 of 2017 with respect to criterion pollutants PM10 and PM2.5 these are still above what is established by the WHO, leaving a trail of uncertainty associated with the true risks to human health. Above all, when there is no standardized recommended minimum, only thresholds that vary in each country depending on its social, political and economic situation.

In this study were identified some limitations: 1) There is an absence of complete time series data for PM10 at a high temporal resolution for all years, and there are only three new stations providing data for PM2.5; 2) Concerning the IRA data is aggregated at the municipality level, rather than at a more granular level like neighborhoods, which limits the potential for in-depth spatial analysis, and the available data series are not extensive.

5 Conclusions

The concentrations of PM10 (29.0–59.1 μg/m3) and PM2.5 (12.4–16.9 μg/m3) indicate good air quality according to national standards (annual maximum: 50μg/m3, daily maximum: 75μg/m3), except for occasional episodes of high concentration values (Molinos Santa Marta's absolute maximum: 194.5μg/m3). However, these levels do not comply with WHO recommendations (annual PM10: 15μg/m3 and PM2.5: 5μg/m3) can change this reality since national regulations converge toward WHO standards. The region's socioeconomic factors likely contribute to spatial and temporal variation in PM concentration alongside meteorological conditions. A need for additional monitoring stations is evident to understand concentration patterns beyond port areas and assess the impact of sources such as vehicular traffic. Health impacts are noticeable in Santa Marta and Ciénaga populations due to PM exposure, with current concentration trends on the rise (global trends range from 0.06 to 8.21 μg/m3-yr). Hence, it is important to conduct studies on the chemical composition of aerosol sprays.

CRediT authorship contribution statement

Eliana Vergara-Vásquez: Writing – review & editing, Writing – original draft, Supervision, Investigation, Formal analysis, Data curation, Conceptualization. Luis M. Hernández Beleño: Writing – original draft, Investigation, Formal analysis, Data curation. Tailin T. Castrillo-Borja: Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation. Tomás R. Bolaño-Ortíz: Writing – review & editing. Yiniva Camargo-Caicedo: Writing – review & editing. Andrés M. Vélez-Pereira: Writing – review & editing, Writing – original draft, Validation, Supervision, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgment

The authors would like to thank J Hani Cause and CORPAMAG for the information necessary to develop this article. Grant UTA-Mayor 5859–23 from Universidad de Tarapacá.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36284.
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