1. Introduction
Aggregate and limestone mining, albeit beneficial to society, comes at a high environmental cost. Mining locations are typically chosen by taking into consideration the economic costs, thereby impacting the health and well-being of the population living in the immediate vicinity. Excavation procedures, crushing and grinding, heavy truck movement, associated truck and noise increments, and generation of dust are some of the detrimental impacts of this industry [
1,
2,
3,
4]. After the excavation and other associated processes, mine tailings can be a blemish in the community, in addition to the fugitive emissions from these tailings due to wind erosion [
5].
One of the most rapidly expanding metropolitan areas in the United States is the San Antonio region of Texas (SAR), specifically counties such as Bexar, Comal, Hays, and Travis [
6]. Mining operations such as sand, gravel, and crushed stone have taken a foothold in this region in the last few decades. Between San Antonio and Austin, accessible limestone aggregate resources are abundant. Major open-pit mines for limestone aggregate, or “quarries”, have expanded North and West of Highway Interstate -35 (IH-35), also identified as the “quarry row”. Heavy truck traffic transporting the material for aggregate mining also results in vehicular air pollution [
7,
8].
Air quality concerns in the SAR region relate to an increase in aggregated mining in recent years. Mining is summarized as rock fragmentation during which particulate matter (PM) is emitted. Particles found in the air—dust, smoke, dirt, and soot—are labeled as PM. Inhalation and mobility depend on the size of the particles [
9]. Particles less than 2.5µm (PM
2.5) travel further into the respiratory system beyond the bronchi where gas exchange occurs [
10]. PM
2.5 mining contaminants are associated with arsenic (As), mercury (Hg), lead (Pb), and considerable amounts of crystalline silicon dioxide [
1,
11,
12]. Long- and short-term exposure to PM
2.5 risks the development of scarred lung tissue (namely silicosis), cardiovascular effects, and other harmful respiratory symptoms [
13,
14,
15]. Additionally, PM smaller than 10µm (PM
10) has adverse effects on the respiratory system. PM
10 components can contain silica and coal dust, causing silicosis or pneumoconiosis [
4,
16,
17]. Therefore, exposure to such PM species poses serious health concerns and warrants attention.
Hence, to address the pressing issues above, this air pollution study aimed at characterizing particulate matter pollution was conducted in the late summer of 2019 in the Northern San Antonio region. Assessing the differences in particulate matter concentrations between the studied sites and central ambient monitoring sites was another aim of this research endeavor. Additionally, the spatial and temporal variation in PM pollution was studied in this research.
3. Results
3.1. 1-h and 24-h PM Concentration Analyses
Temporal variations and descriptive statistics of ambient PM species PM
1, PM
2.5, PM
4, PM
10, and total mass concentration at the four air sites in San Antonio are presented as hourly concentrations in
Table 4 and
Figure 4. Additional statistics that involve correlations or comparisons to the CAMS sites are shown in
Figure 5, and
Table 5 and
Table 6.
Collectively, PM1 and PM2.5 levels were low at all four sites in contrast to the PM4 and PM10 levels. Therefore, PM concentrations in Bexar and Comal Counties are primarily impacted by mining activities. For instance, the seven-day average for PM2.5 was about 8.6 µg/m3 at the ranch, and PM10 values were around 15.8 µg/m3. PM species were highest at the residential compound (S3), with mean values of PM1 18.67 µg/m3, PM2.5 19.70 µg/m3, PM4 20.52 µg/m3, and PM10 23.06 µg/m3. This could be attributed to the close proximity that S1 had to an active mining area. The lowest PM variant concentrations was at the ranch surrounded by a medium-growth forest.
The time series in
Figure 4 conveys the patterns for hourly PM species and total mass concentration fractions (µg/m
3) for each sampled site. The dates are labeled appropriately on each time series to directly portray the temporal variations. The resulting time series aid in understanding the temporal pattern during the sampled period. Ambient data were collected by TSI DustTrak Environmental Monitor to express similar results as the basic hourly statistics listed in
Table 4. The time series at S3 had the highest PM
10 values, second only to the total mass concentration. During the data collection for S4, the DustTrak Environmental Monitor collected only two hours of data for the date 6 September 2019 but was still included in the study.
Boxplot variations of 24 h and hourly average concentrations of PM
2.5 (µg/m
3) are devised from the TCEQ C1069 and each of the four studied air sites in
Figure 5. Hourly boxplots were plotted for a more precise representation of every hour during the 7-day study period. The hourly data were converted into 24 h PM
2.5 concentrations and further compared to the 24 h C1069 data during the duration of each site. The boxplots were created by R programming software and display the mean, median, maximum, minimum, interquartile range, and any outliers.
3.2. Coefficient of Divergence Analysis
Ambient exposure to PM
2.5 between CAMS site C1069 and each studied air site was calculated into COD values as shown in
Table 5. COD values < 0.20 are identified to have similar concentrations between the two sites. COD values > 0.20 determine significant differences in spatial heterogeneity and concentrations between sites. Therefore, according to
Table 5, there are significant differences portrayed between C1069 and S1, S2, S3, and S4 since all the values are > 0.30. The highest COD value is between C1069 and S4 (0.38), the elementary school, indicating a higher level of spatial heterogeneity in pollutant concentration. The lowest COD value is 0.32 between S2 and C1069. These COD values suggest that the PM
2.5 concentrations typically obtained from central ambient monitoring sites, such as C1069, may not be an accurate representation of actual exposure at the neighborhood level.
3.3. Spearman’s Correlation Coefficient Analysis
Spearman’s correlation coefficients were also computed to study the temporal relationships between the PM species at each of the sites (S1, S2, S3, and S4) and the nearest respective CAMS sites (C1069, C504, and C505). These correlation coefficients are presented in
Table 6 with * indicating correlations as statistically significant at 0.05 level (two-tailed test) and ** representing correlation coefficients significant at 0.01 level (two-tailed test). PM species at S1 and S3 were correlated with CAMS sites parameters: CO, NO, NO
2, NO
x, PM
2.5, OT, and O
3; PM species at S2 and S4 were correlated with the available CAMS sites parameters of CO, NO
2, PM
2.5, OT, and O
3.
Across the four samples sites, the various PM species were very strongly correlated with each other with r > 0.942, p < 0.01. This suggests that mining activity is a major contributor to these PM species concentrations. Additionally, the correlation coefficient between NO2 and the PM species at sites S1 and S2 is negative, thereby suggesting that there are no common sources for these two pollutant groups. However, for site S3, NO was weakly correlated at the p < 0.01 level with PM1 (r = 0.281), PM2.5 (r = 0.303), PM4 (r = 0.313), and PM10 (r = 0.271), suggesting the role of possible NO emissions from construction and other mining equipment. Site3 was a residential site with moderate vegetation and located near a quarry; therefore, it may be posited that some natural biogenic sources along with combustion fuel emissions from mining machinery near this site could be attributed to NO.
4. Discussion and Conclusions
As per our knowledge, this study is the first of its type to characterize ambient PM species in the SAR impacted by aggregate and limestone mining. The results confirm that PM concentrations in the San Antonio counties, i.e., Bexar and Comal, are impacted by open-surface mining activities. Primarily, PM
1 and PM
2.5 concentrations are lower in contrast to PM
10 levels. Exposure to high amounts of PM
10 components results in short- and long-term health effects [
23,
24]. The maximum amount of PM species was found in the residential compound sampling area in this study. This could be attributed to its proximity to the open-surface mine.
Spearman’s rho correlation was used to analyze the strong relationships between PM species in each site. Similarly, COD analysis confirmed the PM spatial heterogeneity and concentrations measured were different from the TCEQ monitored CAMS site C1069. Our findings, therefore, accentuate the fact that central ambient monitoring sites at the intra-urban level are not a true representation of exposure patterns due to PM pollution generated by anthropogenic activities, such as mining operations.
Many studies characterizing the environmental impact of mining and quarrying activities have been undertaken throughout the world. A study conducted in Southeast Spain showed that high ambient levels of PM could be attributed to activities such as mining and quarrying [
25]. Another study by Khademi et al. from Murcia, Spain, suggested the high environmental and health risk of windblown dust from mining ponds etc. and suggested remedial measures such as plant vegetation to minimize the mining impacts [
5]. A North Jordanian study documented high levels of PM
10 (120–140 µg/m
3) in urban areas surrounding the limestone quarries during the late summer months of July and August [
26]. A study from Taiwan analyzing fugitive dust emissions from gravel processing sites showed that PM
10 concentrations ranged from 135 to 550 µg/m
3 and PM
2.5 concentrations ranged from 105 to 470 µg/m
3 [
27]. Findings from these studies and the present research work demonstrate the importance of taking all the precautions necessary to offset the environmental and health effects of mining activities.
It is also important to mention some of the limitations of our study. Every site was sampled for only one week each during late summer. Future studies in this region should also consider PM sampling during other seasons in addition to extending the sampling time of the study. Furthermore, our study did not measure any elemental composition of the various PM species due to pecuniary challenges. Nevertheless, we believe that studies such as ours are instrumental in addressing the topic of particulate matter air pollution in semi-urban residential environments.
Finally, based on the results from this research work, we posit that all necessary precautions should be undertaken to minimize the effects of fugitive dust emissions from such mining operations in the San Antonio area of Texas, USA. We suggest that the various stakeholders such as the local county officials, mining personnel, and the affected residents should conduct a health impact assessment study due to mining activities and formulate policies that would incorporate the basic principles of sustainable development, thereby mitigating the deleterious health effects and ameliorating the overall concerns of the community at large.