Air Quality and Social Vulnerability: Estimating Mining-Induced PM₁₀ Pollution in Tula, Mexico

Abstract

The Tula Metropolitan Area in Mexico is characterized by significant industrial activity, including thermoelectric power plants, refineries, cement plants, and mining operations. While the impact of mining on air quality has been less studied compared to other industries, this research aims to estimate the contribution of mining areas to PM₁₀ air pollution in the region. Using the AERMOD dispersion model coupled with the WRF meteorological model, emission areas were identified through GIS analysis, and specific emission factors for mining activities were applied. The results indicate that mining areas can contribute up to 40 µg/m³ of PM₁₀, exceeding both national and international air quality standards. Monitoring data suggests that mining activities account for approximately 30% of the measured PM₁₀ concentrations in the area. Furthermore, spatial analysis using the Urban Marginalization Index (UMI) revealed that areas with high PM₁₀ concentrations often coincide with regions of high social vulnerability, particularly in communities with elevated levels of marginalization. This study concludes that mining operations significantly contribute to air pollution in the Tula Metropolitan Area, highlighting the need for targeted mitigation measures and public policies that address both environmental and social vulnerabilities.

1. Introduction

Air pollution in urban areas significantly influences persons’ daily live [1]. This persistent air quality degradation [2] is supported by overwhelming evidence of its detrimental effects on human health and societal well-being. In response, the World Health Organization (WHO) [3] has established guidelines [4] to address this pressing issue and protect public health.

Among air pollutants, particulate matter is especially relevant because 99% of the world’s population is exposed to harmful levels of particulate matter [5], and it is the main contributor among risk factors of the total burden of disease [2].

Particulate matter (PM) in air is categorized by size, with PM₁₀ referring to particles 10 μm or smaller and PM_2.5 referring to particles 2.5 μm or smaller. Once inhaled, PM₁₀ is more likely to deposit on the surfaces of the larger airways of the upper region of the lung, while PM_2.5 is more likely to travel into and deposit on the surface of the deeper parts of the lung.

Short- and long-term exposure to PM₁₀ and PM₅ are associated with the worsening of respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD) and lung cancer [6]. Similarly, PM_2.5 air pollution exposure is associated with acute lower respiratory infections, COPD, ischemic heart disease, lung cancer, type II diabetes, neonatal low birth weight and short gestation [5].

Environmental impacts of particulate matter include changes in water, soil and air quality and ecosystem degradation, habitat loss, changes in land use patterns and aquifer regimes [7,8].

Urban areas are particularly vulnerable because they have multiple pollution sources, including industry, transportation, energy, and housing. In this context, the presence of quarries around urban areas can contribute to particulate air pollution.

Particles in the limestone mining industry are emitted during excavation, crushing, grinding, material movement and tailings wind erosion [9,10]. PM₁₀ is a primary indicator of air quality in mining areas [11]. The high concentrations of this pollutant are a major health concern [12,13] due to the particles’ ability to adsorb a variety of toxic metals [14] and the capacity of PM_2.5 to penetrate deeply into the human lung system, causing substantial pulmonary damage [15]. Therefore, it is necessary to quantify the impact of these sources of anthropogenic pollution so that effective controls can be put in place to improve air quality.

Tula Metropolitan Area

The Tula metropolitan area is formed by four municipalities located in central Mexico; its total surface area is 673 km² and the median altitude is 2020 m above sea level; the mean temperature is 16.7 °C, and the annual average rainfall ranges between 435 and 618 mm from May to October [16]. According to the Mexican legal framework, this metropolitan area is a critical zone due to its levels of air pollution [17].

The Tula Metropolitan Area in Mexico is an industrial corridor of national significance, characterized by a high concentration of pollution sources [18]. Among the main ones are the Miguel Hidalgo Refinery of Mexican Petroleum (PEMEX) and the Francisco Pérez Ríos Thermoelectric Power Plant of the Federal Electricity Commission of Mexico (CFE), which emit compounds such as SO₂, NO₂, and particulate matter [19]. Additionally, the region shows intense mining activity, particularly limestone extraction [20], which, together with other industries such as cement, metallurgy, and food processing [21], significantly contributes to air quality degradation. The synergy between diverse emission sources makes the Tula Metropolitan Area a case study of complex air pollution (Figure 1).

Previous studies in the area have studied Tula complex emissions using remote sensing in 2006 [22] and 2009 [23] and have modeled the dispersion of pollutants in the Miguel Hidalgo Refinery in 2012 [21] and 2014 [19]. Characterization of air pollutants in the study area was established in 2012 [20] based on the MILAGRO monitoring study that took place in 2006, which identified quarries as a major contributor of particulate matter; however, the present study is the first attempt to quantify the contribution of PM₁₀ from limestone mining in the area.

The importance of the study relies on the fact that despite adjustments to operational conditions at the refinery and thermoelectric plant, with the latter shifting towards natural gas consumption [24], the Tula Metropolitan Area continues to experience exceedances of Mexico’s air quality standards, primarily for PM₁₀ and SO₂ [25]. This suggests that other sources, such as particle matter area sources, contribute to air pollution.

The environmental implications resulting from industrial and extractive activities have been extensively documented in academic literature and by non-governmental organizations (NGOs). These regions have been identified as “sacrifice zones”, a term used to describe areas where anthropogenic activities lead to irreversible environmental degradation, significant public health deterioration, and elevated concentrations of pollutants. Such impacts are the consequence of sustained and intensive exploitation of natural resources and territorial space [26,27]. This phenomenon is particularly prevalent in densely urbanized regions, such as the Megalopolis of the Valley of Mexico. Within this area lies the Mezquital Valley, also referred to as the Tula Metropolitan Area, which constitutes the case study examined in this research [28].

The Mezquital Valley academic studies had focused their efforts on the petrochemical industry’s impact on water [29] and land [30,31] and biodiversity [32], but there are no studies focused on the particular impacts of non-metallic mining activities; while there are some approaches on the environmental impacts in this area, most are systemic. Consequently, it is essential to estimate the impact of mining and cement production areas [33] on air quality. In addition to air quality monitoring, a dispersion modeling study is required to assess the impact, especially to aid in decision-making and the implementation of measures to reduce air pollution. Therefore, the primary objective of this study is to quantify the contribution of open-pit mining activities to air quality within the Tula Atmospheric Basin and the evaluation of the social impact of PM₁₀ in the study area.

2. Materials and Methods

The primary objective of this study is to quantify the contribution of wind erosion from open-pit mining areas to air quality in the Tula Atmospheric Basin. A methodology is proposed (Figure 1) that involves (1) delimitation of the study area based on the official definition of the Tula Atmospheric Basin and spatial analysis using GIS to identify areas with open-pit mining activities; (2) estimation of PM₁₀ emissions from wind erosion in exposed mining areas; (3) simulation of atmospheric dispersion using the coupled WRF-AERMOD modelling system; and (4) comparison of modeled concentrations with ambient air quality monitoring data and determination of the relative contribution of mining activities to air quality.

2.1. Delimitation of the Study Area

The study area, the Tula metropolitan region, has been identified as an Atmospheric Basin by the Mexican government’s National Institute of Ecology and Climate Change (INECC) due to its elevated levels of air pollution. Located in southern Hidalgo state, the basin is centered at a Latitude of 20.108302° N and a Longitude of −99.299597° W [34]. The complete area consists of 12 municipal administrations where 371,926 people are living in 43 urbanized areas and 6 cities, according to the 2020 population census [35]; some of them have densities above 36,000 inhabitants/km² and urbanized land use of more than 3 km wide, even though administrative borders. There is a high presence of industrial activities, including two industrial zones and an oil and gas refining complex operating in the area (Pemex’s Miguel Hidalgo Refinery complex). There are also 5 ecological preservation areas, one federal conservation park inside the study area, two forest conservation areas and some private ecological sanctuaries, mostly focused on hot springs and ecotourism.

The main agricultural activities are concentrated north of the city of Tula, in the center of the study area around the Xicuco hill in the Tezontepec de Aldama municipality, and in most of the Mixquiahuala de Juarez municipality’s land, with some other spaces around rural urbanization areas in the eastern part of Atitalaquia’s municipality, while the northwest and south are more driven into natural ecosystems where low accessibility is common due to the mountain areas and ravines that supply the water dams present in the areas around Sayula town, Santa Maria Macua, Tepeiticm Texcatepec, Chilcuautl and San Luis Taxhimay. The Mexican National Institute of Statistics and Geography’s land use and vegetation database was used to analyze areas devoid of vegetation. Subsequently, these areas were examined for mining activities (Figure 2). The surfaces identified with this characteristic that are in the Tula atmospheric basin were selected and inputted into the model.

2.2. Emission

Previous studies have demonstrated the feasibility of assessing and quantifying particle emissions from mining activities [36,37], even methodologies for calculating the emission rates of toxic metals from silver mining operations [38], potential hydrogen cyanide from mine tailings [39], smelter slag [40], phosphate [41] and methane [42]. Therefore, it is crucial to continue conducting studies, calculations, estimations, and modeling to acquire more accurate and trustworthy data. Particulate matter emissions from processes can be estimated [43] using the following equation:

PME_process = (PMEF_process) (Unit_process) (1 − EC/100),

where

• PME_process = particulate matter emissions for a given process.
• PMEF_process = particulate matter emission factor for a given process.
• Unit_process = tons processed, tons produced, tons transferred, etc.
• EC = emission control factor, %

Emission factors were obtained from Elson and Ince [44]. Given the arid and semi-arid climate of Tula, wind erosion of open-pit mining is an emission source concern. In such environments, water scarcity and intense winds can worsen the susceptibility of areas to erosion [45]. For the present work, the emission control factor is set to zero.

2.3. Modeling System

2.3.1. AERMOD

The dispersion of PM₁₀ due to wind erosion in these types of mining areas can be modeled [46]. The American Meteorological Society (AMS) has developed an air quality dispersion model called AMS/EPA Regulatory Model (AERMOD) [47]; although AERMOD might underestimate concentrations [48], AERMOD has generated better results than other models such as CALPUFF [49]. AERMOD has the capability to simulate dispersion from rural and urban areas, flat and complex terrain, surface and elevated releases, and multiple sources [50]. AERMOD is composed of three modules: AERMET, integrating surface and vertical meteorological data; AERMAP, modeling terrain and receptor elevations; and AERMOD, which inputs emission data and source characteristics. In this paper, every surface identified as being vulnerable to wind erosion was represented in the AERMOD dispersion model as an open pit mine. The emission rate determined in the preceding section was held constant for the entirety of the 3-year simulation.

2.3.2. Meteorology (WRF)

AERMOD uses hour-by-hour meteorological data that is processed through AERMET, where the meteorology data are organized into a suitable format for the AERMOD dispersion mode; nevertheless [51], in Mexico, as well as in many other countries, there are regions where it is difficult to install and operate weather stations by which to obtain surface and vertical weather information [52]. For this reason, it is necessary to reanalyze meteorological data and meteorological models to overcome this complication. The Weather Research and Forecasting (WRF) Model can be coupled with various dispersion models such as the Atmospheric Dispersion Modeling System (AERMOD), Community Multiscale Air Quality model (CMAQ), and the Advanced, Integrated LaGrange Puff Modeling System, also known as CALPUFF [53]. WRF is characterized as “a next-generation mesoscale numerical weather forecast system” by the National Center for Atmospheric Research (NCAR) [54].

Table 1. Weather modeling parameters.

Element	Parameter
Forecasting modeling system	WRF
Resolution	12 km
Study area	50 km
Period	1095 days (2021–2023)
Database	NCEP Climate Forecast System version 2 [55]
Reference coordinates	Latitude 20.057°, Longitude −99.278°

shows the parameters used in weather modeling configuration.

The modeling included three years (2021–2023) for each hour and the following variables: sensible heat flux (W/m²), surface friction velocity (m/s), surface roughness (m), convective velocity (m/s), convective and mechanical planetary boundary layer height (m), Monin–Obukhov length (m), Bowen radius, albedo, surface temperature (K), wind speed (m/s) and direction (°), precipitation (mm), relative humidity (%), surface pressure (mb) and cloud cover. For the meteorology in the vertical direction, the following were obtained: wind speed (m/s) and wind direction (°), surface temperature, and standard deviations of wind speed and direction at different altitudes, from 175 to 5000 m above sea level.

2.3.3. Evaluation of Sensitivity

In this article, given that the simulations and estimates of the impact on air quality due to wind erosion from open-pit mines in the Tula atmospheric basin are being conducted, it is proposed to evaluate the sensitivity of the WRF meteorological model. Given the lack of reliable and validated meteorological information in the area, it was decided to use the European Centre for Medium-Range Weather Forecasts (ECMWF) database, which is made up of reanalysis data combining information from weather stations, climate data, aircraft, buoys, radar, and satellite observations. The statistical parameters considered are the root mean square error (RMSE), the normalized mean absolute error (NMGE), the mean bias (MB), Pearson’s correlation coefficient (r) and the index of agreement (IOA) [52]. The variables used in the sensibility analysis were temperature (T), relative humidity (RH) and wind speed (WS). The WRF meteorological model output for the three years under analysis generates 26,280 hourly data points per variable. These outputs are compared against a reference dataset to assess the model’s sensitivity.

2.3.4. AERMOD Configuration

As part of the terrain analysis, it is important to note that AERMOD is applicable and recommended for simulations with transport up to 50 km [45]. Therefore, after finding the atmospheric basin, an area with a higher concentration of mining zones will be selected to generate the computational grid.

Table 2. AERMOD modelling parameters.

Element	Parameter
Digital model	Shuttle Radar Topography Mission (SRTM)
Resolution	30 m
Mesh	50 km
Spacing	500 m
Receptors	10,201
Period	1095 days (2021–2023)
Average concentration	1 h
Reference coordinates	Latitude 20.057000° N, Longitude −99.278000° W

shows the parameters used in AERMOD modelling configuration.

Additionally, each site is considered an area source with the AERMOD open pit configuration, estimating that the height of this surface is constant relative to the modeled ground elevation.

2.4. Standards for Evaluation of Air Quality by PM₁₀

To assess the impact on air quality in the Tula atmospheric basin, air quality standards from the World Health Organization (WHO), the European Union (EU), and the Mexican Official Standard (NOM) were employed. Given that the United States Environmental Protection Agency (EPA) does not have a standard for annual average concentrations, it was not considered for comparison.

Three years were simulated (2021 to 2023), and the maximum annual average concentration from the dispersion model was considered as the evaluation metric for the impact on air quality. Additionally, a map was created showing the annual average concentrations of the dispersion plume to analyze the behavior of the contribution from the sources evaluated in this article.

Table 3. Air quality standards.

Air Quality Standard (PM10)	Averaging Period	Concentration (µg/m3)
OMS (2021) [56]	Annual	15
EU (2024) [57]		20
NOM (2021) [58]		20

shows the air quality standards for PM₁₀.

To evaluate PM₁₀ contributions to air quality in the Tula Atmospheric Basin, five monitoring stations were reviewed, four within the Tula metropolitan area. Data were sourced from Mexico’s official air quality system. The Atotonilco station, centrally located among emission sources, was selected for analysis due to its compliance with the 75% data completeness requirement (NOM-156-SEMARNAT-2012). Monitoring was conducted hourly throughout the year by the Sistema Nacional de Información de la Calidad del Aire (SINAICA) [Mexican Air Quality Information System].

2.5. Social Impact Evaluation

The social impact of the dispersion of the pollutant was evaluated using a bivariate local Moran I index calculation [59]; for this purpose, a normalized hexagonal grid with 0.5 km distance between centroids was created; pollution, population [35] and Urban Marginalization Index (UMI) [60] data were gathered on the grid to homologate spatial coverage between variables. This was calculated using the following equation:

$$I_b={\displaystyle \frac{{\sum }_i\left({\sum }_j{w}_{ij}{y}_j\ast x_i\right)}{{\sum }_i{x}_i^2}}$$

where

• w_ij = binary weight that indicates neighborhood adjacency of j samples to i.
• i = the intersection in question.
• j = their neighboring intersections of i.
• y = the dependent variable.

The spatial neighborhood matrix considered a 2nd-order queen contiguity with lower-order inclusion to adapt the analysis to the dispersion ranges for the three variables. Statistical significance was considered using permutation inference with 999 iterations with a standard normal distribution assumption.

Maps presented in this article were made using QGIS software version 3.40 [61].

3. Results

In accordance with the previous section, the results are presented in this section. Firstly, the results of the study area and emissions are shown. Secondly, the evaluation of the sensitivity of the meteorological model and main variables is presented. Finally, the estimation of the impact on air quality in the Tula atmospheric basin is presented.

3.1. Emissions in the Study Area

Through this process, 13 areas with mining activities were identified. However, it is important to note that other areas within the atmospheric basin were identified where the extracted material is processed, as well as cement plants, which contribute to air pollution.

Thirteen areas with mining activities within the Tula metropolitan area (Figure 3 and Figure 4), located in the south-central portion of the atmospheric basin, were selected for this study. This selection was based on one key factor: the presence of air quality monitoring stations within the metropolitan area.

Table 4. Emissions sources.

Zone	Reference Coordinates (Latitude, Longitude)		Area (km2)	Emission (g/s)
1	19.972773° N	−99.361018° W	0.30	0.42
2	20.009011° N	−99.296722° W	1.00	1.40
3	19.959346° N	−99.280095° W	0.59	0.83
4	19.977837° N	−99.278636° W	0.28	0.39
5	19.976077° N	−99.271331° W	0.67	0.94
6	19.986662° N	−99.269256° W	0.35	0.49
7	19.988643° N	−99.262271° W	0.62	0.87
8	20.015981° N	−99.228319° W	0.55	0.77
9	19.966233° N	−99.221968° W	1.71	2.39
10	19.990308° N	−99.220969° W	1.08	1.51
11	19.994019° N	−99.183703° W	0.35	0.49
12	19.988484° N	−99.179385° W	0.74	1.04
13	19.962665° N	−99.180579° W	1.28	1.79

shows the emission rate inputted into the model for each emission source, along with the reference coordinates and the surface area subject to wind erosion (1.40 × 10⁻⁶ g/m²). The 13 areas with mining activity are characterized by sites with extraction zones for construction materials, quarries, deposits, and material processing sites. These zones are geographically situated at higher altitudes compared to human settlements. This condition, coupled with meteorological variables, promotes the dispersion of particulate matter. Additionally, the Mezquital Valley is characterized by a desert ecosystem with low precipitation. Furthermore, the companies operating in these zones lack mitigation measures to prevent the dispersion of said particulate matter.

3.2. Sensitivity Evaluation WRF

Table 5. Sensitivity evaluation WRF.

Year	Variable	Temperature	Relative Humidity	Wind Speed
2021	MB	−0.81	2.63	0.87
	NMGE	0.11	0.16	0.59
	RMSE	2.44	14.2	1.66
	r	0.91	0.87	0.65
	IOA	0.78	0.78	0.46
2022	MB	−0.63	1.78	0.85
	NMGE	0.11	0.16	0.58
	RMSE	2.37	14.3	1.68
	r	0.92	0.86	0.67
	IOA	0.80	0.77	0.47
2023	MB	−082	3.02	0.86
	NMGE	0.11	0.18	0.61
	RMSE	2.52	15.4	1.75
	r	0.92	0.84	0.62
	IOA	0.80	0.75	0.45

shows the sensitivity evaluation of the meteorological database used as input for the model compared to data recorded at the on-site monitoring station. The meteorological database, calculated using the WRF mathematical modeling system, has a spatial resolution of 12 km, meaning it is an average over this area.

The data shows a high degree of agreement with the values measured at the meteorological station, particularly for temperature, relative humidity, and wind speed. A slight improvement is observed in the prediction of temperature and wind speed in 2022 and 2023, while the prediction of relative humidity remains stable. These results suggest that the model may be a useful tool for modeling atmospheric emissions from mining areas due to wind erosion.

3.3. Impact on Air Quality by PM₁₀

According to the modeling results obtained using the AERMOD dispersion model coupled with WRF and the wind erosion factors employed, the estimated annual average is 19.2 µg/m³, considering a three-year meteorological period (2021–2023); the data presented in Table 5 show that mining areas within the Tula metropolitan area can contribute to air quality with PM₁₀ concentrations exceeding the threshold of the compared standard of the WHO.

Analysis of the AERMOD-calculated concentrations indicates an annual average concentration of 19.2 µg/m³ (

Table 6. Impact on air quality by PM₁₀.

Month	Impact to Air Quality (µg/m3)
	2021	2022	2023
January	21.9	19.8	20.2
February	16.3	20.5	15.3
March	14.3	17.8	17.9
April	16.7	16.2	15.9
May	16.4	16.5	14.9
June	20.1	18.4	16.2
July	23.5	21.7	21.3
August	19.9	24.0	22.1
September	22.1	20.5	22.9
October	19.5	18.1	21.3
November	20.0	21.1	20.5
December	20.0	20.4	19.1
Annual	19.2	19.6	18.9
AQS comparison (%)
15 (OMS)	+28.0	+30.6	+26.0
20 (EU)	−4.0	−2.0	−5.5
20 (NOM)	−4.0	−2.0	−5.5

). However, maximum daily average concentrations reached 53.1 µg/m³, with monthly averages peaking at 24 µg/m³. Furthermore, the 99th percentile of hourly concentrations over the three-year period was determined to be 41.8 µg/m³ (Figure 5). It is crucial to note that this study solely considers the contribution of mining activities to air pollution. Other sources are not included in this assessment. Consequently, while the mining-related source alone surpasses the World Health Organization (WHO) guidelines, it is anticipated that additional sources would further contribute to exceeding the maximum permissible limits stipulated by both the NOM and EU. It is important to note that, although the WHO threshold is exceeded, the NOM is the standard used in Mexico to assess air quality.

3.4. Social Impact of the Pollutant

Bivariate Moran I index between pollutant dispersion and social variables shows a negative global light correlation between pollutant spatial dispersion, population density and UMI (−0.209 and −0.11, respectively).

At a local level, the clusterization shown on Figure 6 maps gives evidence of a general spatial tendency to be in mutually exclusive places for the pollution impact and the population spatial dispersion; nevertheless, UMI has a positive correlation with the pollutant dispersion mostly in suburban areas around the Atotonilco de Tula and Conejos localities, while low UMI areas with high pollution are around the Apaxco de Ocampo locality. The most impacted area in terms of affected population produced by mining activities can be outlined around Atotonilco de Tula, where spatial correlations are high between the social variables and the modeled pollution, especially in the corridor between the sources and the locality, where patchy spatial patterns can be seen as the result of informal settlements on the urbanization process.

4. Discussion

The Tula atmospheric basin is a complex study area due to its industrial activities, necessitating comprehensive investigations into all emission sources within the basin. While this article does not account for various processes within mining activities, cement plant emissions in the study area, agricultural activities, unpaved roads, and other potential emission sources, the results obtained demonstrate a significant contribution of PM₁₀ from mining areas due to wind erosion. Visualization of the dispersion plume behavior of PM₁₀ concentrations reveals a correlation between these emission sources and the high concentrations recorded at the Atotonilco station.

The modeled maximum daily average concentrations are consistent with PM₁₀ measured in the nearby limestone quarries of 23.06 µg/m³ in Texas, USA [9]. Seasonal variations with high concentrations during the summer months have also been reported in limestone quarries in a study in northern Jordan [62]. Transport of dust 1 km downwind of the quarry was also reported in the Kefar Gil’adi quarry in Israel [63]. Other studies have reported higher concentrations of PM₁₀ ranging from 135 to 550 µg/m³ at gravel processing sites and 825 μg/m³ during the crushing process in Taiwan [64,65].

A query of the Sistema Nacional de Information de la Calidad del Aire (SINAICA) [Mexican Air Quality Information System] for the period 2021 to 2023 identified the Atotonilco station as meeting the 75% data sufficiency criterion. Analysis of these data revealed that PM₁₀ concentrations in the Tula Metropolitan Area exceeded the regulatory limits for the protection of public health during this period. An annual average concentration of 77.8 µg/m³ was determined, exceeding the WHO, NOM, and EU air quality standards. Furthermore, based on the annual average concentrations estimated in this study, it is estimated that these areas could contribute up to 24% of the PM₁₀ in the Tula Metropolitan Area. However, concentrations can exceed national and international standards in areas close to the emission source. Long-term exposure to these fine particles has been linked to an increased risk of respiratory and cardiovascular diseases.

It is important to note that the use of atmospheric dispersion models such as AERMOD may carry a margin of error of at least ±10% [66]. Thus, it is reasonable to expect that the concentrations calculated in this study fall within this range. While dispersion models have limitations, they aid in estimating concentrations and emission behaviors. Therefore, further studies, analyses, and investigations, such as PM₁₀ sampling to characterize these pollutants and pinpoint emission sources, are recommended to enhance our understanding of the problem.

The analysis of the clustering map of the spatial correlation between the dispersion plume generated by erosion from mining activities in the region and the Marginalization Index and population reveals a high correlation. This indicates that the impact on air quality is strongly associated with areas of extremely high marginalization, representing a significant public health issue.

From a social impact perspective, this relationship can be explained by the fact that such activities tend to be established in areas with high marginalization or, alternatively, encourage the growth of informal settlements in their vicinity. This suggests that in these peripheral sectors, where urban growth is less planned, pollution tends to coincide with areas of greater social vulnerability in localities such as Atotonilco, Ocampo, El Puertecito, La Cañada y Conejos.

On the other hand, in suburban areas, the presence of settlements with better infrastructure and access to services is observed. However, due to their proximity to industrial and mining activities, these areas are exposed to elevated levels of pollution. The area most affected in terms of population exposed to mining activities is in Atotonilco de Tula, where the spatial correlation between social variables and modeled pollution is high. A corridor between the polluting sources and the locality stands out, where fragmented spatial patterns are observed. This distribution suggests the presence of informal settlements undergoing urbanization, which could exacerbate the vulnerability of these communities to pollution. These findings highlight the importance of developing public policies that consider not only pollution mitigation but also urban planning and infrastructure development in vulnerable areas. A more detailed assessment of the environmental impact in growing suburban areas is recommended, along with the implementation of strategies to reduce exposure for the most affected communities.

Several studies have demonstrated that meteorological factors, such as relative humidity, precipitation, wind speed, and wind direction, affect the concentration and dispersion of airborne particles. However, land use and human activities, such as mining, also have a significant local impact on these concentrations [67]. It has been shown that the implementation of control measures can reduce PM₁₀ concentrations by between 60% and 99% [68] (B).

Although this study focused on estimating the contribution of mining areas to PM₁₀ emissions in the Metropolitan Area of Tula over a three-year period, future research should assess other relevant pollution sources, such as cement plants, which generate significant particulate emissions during production and material transport, as well as particle resuspension linked to vehicular traffic in the region. Furthermore, it would be pertinent to investigate the effect of climatic anomalies or extreme meteorological events, such as prolonged droughts or atypical dust storms, which may alter PM₁₀ dispersion patterns and concentrations.

The significance of these results indicates the need to implement a mixed strategy since some of the sources analyzed in the present study are considered as passive environmental sources. This requires an integral strategy where the three levels of government, mining companies and the main cities that benefited from the extraction need to be involved to obtain the financial cost of implementation strategies that improve air quality in the basin and also environmental measures aimed at significantly reducing erosion. These measures include vegetation cover, diversion ditches, drainage systems, soil compaction, geomembranes, synthetic covers, soil moistening, or the application of modified lime [69].

Considering these elements, the proper assessment of economic costs and reach for the ecological remediation of the Mezquital Valley impacted areas must be addressed considering not only the economic value of the mining activities; it must address as part of the problem the social, health and environmental impact on the communities (which mining activities alone constitute almost 95% of the legal threshold for yearly average), including the installation of proper monitoring equipment for the existent airborne pollution in the area acknowledged by this study and the environmental and social issues identified in other studies for the studied area [26,29,32].

5. Conclusions

This study has assessed the impact of mining activities on air quality in the Tula Metropolitan Area, Mexico, focusing on PM₁₀ particulate emissions. By using the AERMOD dispersion model, coupled with the WRF meteorological model, it has been demonstrated that mining areas contribute significantly to air pollution, with PM₁₀ concentrations reaching up to 40 µg/m³, exceeding the air quality standards established by the World Health Organization (WHO), the European Union (EU), and Mexican regulations (NOM). Furthermore, it is estimated that these activities account for 30% of the measured PM₁₀ concentrations in the region. The results indicate that areas close to mining emission sources experience PM₁₀ concentrations that exceed permissible limits, posing a significant risk to public health, particularly in communities with elevated levels of social marginalization. Spatial analysis revealed a correlation between pollutant dispersion and areas of high social vulnerability, highlighting the need to implement mitigation measures and public policies that address both pollution and urban planning in these areas.

Other research has shown that meteorological, social, land use, and anthropogenic factors significantly influence air quality. While most studies have concentrated on power plants and refineries, less attention has been paid to other sources, such as those present in the Tula area metropolitan, where mining emissions are often underestimated. However, this study reveals a strong correlation between highly marginalized areas and elevated PM₁₀ concentrations linked to mining activities. This observation prompts a critical question: does mining activity drive marginalization in these areas, or does pre-existing marginalization enable the establishment and persistence of extractive industries?

In conclusion, this study underscores the importance of continuing to monitor PM₁₀ particles and other atmospheric pollutants in the region, as well as conducting more detailed analyses to identify specific emission sources. Additionally, it is recommended to strengthen collaboration between environmental authorities and mining companies to develop strategies that improve air quality and protect public health and the environment in the Tula atmospheric basin. The implementation of control and mitigation measures in mining areas is essential to reduce particulate emissions and ensure better air quality for the atmospheric basin, but most importantly, for the affected communities.