The main results regarding the multi-approach described above will be presented in the following sections. All the results are being integrated into the GIS application (Coal Mine) to manage all the data and to relate all the environmental variables monitored.
3.1. Temperature Monitoring
For each flight campaign (Table 1
), a thermal orthomosaic was generated (Figure 6
). For comparative temperature purposes, it was necessary to standardize the color scale.
Temperature monitoring allows the identification of an area that consistently presents higher temperatures, with maximums over 50° C. This area has approximately 200 m2 and is located in the south of the waste pile, near its base. The limits of this highlighted area are consistent with field observations of gas release as a result of self-burning and were validated in situ by a portable infrared thermometer.
In Figure 6
, it is possible to compare the surface temperature measured along the waste pile throughout different seasons of the year, and to note the influence of the air temperature and solar incidence on its surface temperature distribution. The August surface thermal map was collected under higher atmospheric temperatures, showing limitations in the identification and definition of the self-burning areas.
Even though the number of campaigns is reduced, a Pearson correlation was performed to identify if the temperature recorded by the thermal sensor on board the UAV was influenced by the air temperature on the day of the flight. To obtain the temperature values at the same location in all the campaigns, 217 points were defined in the waste pile area. The correlation between the data recorded by the thermal sensor on board the UAV and the air maximum temperature (available from the Instituto Português do Mar e da Atmosfera
]) in each campaign (because the flights occurred near the solar nadir, i.e., the period of the highest incidence of radiation) was 0.84, meaning that the air temperature directly influences the waste pile surface temperature.
A complementary analysis was performed considering the slope and aspect of the waste pile. Figure 7
presents the aspect (exposure) maps generated for each of the campaigns. The thermal orthomosaics from May and August, collected on the days that presented the highest atmospheric temperature and sun exposure, showed that the slopes oriented to the southeast were spatially associated with higher temperature patterns. The data collection was achieved in the late morning period when there was greater solar incidence caused by the sun rising to the east of the study area, causing a higher warming in the materials facing this direction.
It is possible to identify some agreement between the areas with a higher temperature and exposed to the southeast, except for those with vegetation areas, mainly located in the central zone in the upper part of the images. To analyze this relation in more detail, the NDVI (Equation (1)) was computed. Table 3
presents the maximum and minimum NDVI values.
The resulting maps were compared to the thermal orthomosaic (Figure 8
). In the December campaign, due to equipment unfeasibility, it was not possible to collect the RGB data, and, therefore, it was not possible to generate the corresponding NDVI map. From the results obtained, it is possible to observe an inversely proportional behavior between areas with vegetation and surface temperatures.
The methodology selected to monitor the temperature was effective in an approximate identification and delimitation of the area currently under active burning; however, it can be globally influenced by the atmospheric temperature and sun exposure. Under higher atmospheric temperatures, the waste temperature resulting from the burning process can be masked by the combined effects of sun exposure and the atmospheric temperature, which increase the soil surface temperature externally; as a result, monitoring temperature measurements should be avoided on hot days with intense solar exposure.
3.2. Altimetric Variations
In addition to the 217 points already used in the thermal treatment, another 165 points (outside the waste pile, in the surrounding areas) were manually added to the DEM to help in the analysis of altimetric variability, considering stable areas, such as streets and paths [58
]. The DEM values of the five campaigns were compared to these points, and the altimetric variation was evaluated. To conduct this evaluation throughout the collection period, the difference between the altimetric values between the first and last campaigns (July, 2019 and November, 2020) was performed, whose results (number of points and percentage) are shown in Table 4
Considering the altimetric variation analysis, a general decrease in elevation (except for two points) was found. This finding may corroborate the hypothesis that, as self-burning occurs, the material in the waste pile may be reducing. To evaluate this effect quantitatively, the 217 points were separated into four classes, considering the altitude variation scale (Table 5
Most of the points (82.03%—178 points) showed a decrease between 20 and 71 cm. Only two points showed an increase between 10 and 60 cm (points 24 and 213). Figure 9
shows the geographical distribution of the points in the respective altimetric variability scales. The two points that presented an altimetric increase are circled in blue.
For the two points that presented an increase in altimetry, a more detailed analysis was carried out to identify the possible cause of this distinct behavior. These two points are located at the base of the waste pile. The main cause for this elevation increase could be related to material that slides down to the waste pile and is deposited in this area.
3.3. Land Use Land Cover (LULC)
The Micasense RedEdge multispectral sensor recorded data in five different bands (blue, green, red, red edge and NIR). These data allowed computing an unsupervised classification to identify the classes presented in the waste pile. The k-means algorithm was used in SNAP software [59
], which classifies n
observations among k
groups, where each datum is grouped to the values closest to the group average, resulting in a spatial division, following the logic of a Voronoi diagram [60
]. The k-means algorithm belongs to the partitioning clustering methods in which the mean value of the objects or observation in a cluster is used as the center of the cluster, which is also regarded as the center of gravity for a cluster. The k parameter is the total number of clusters to create. The general approach for clustering with the k-means algorithm is regarded as an iterative relocation technique. The objective of the algorithm is to improve the quality of the initial cluster. It implies that cluster membership is changed to find the local optima. The clustering or object criterion (E
) is to minimize the Euclidean sums of squared deviations of objects from the cluster mean [61
Considering that the study site does not present great variability in relation to the LULC, the algorithm firstly identified three classes (two classes associated with soil and one with vegetation). However, in a deeper analysis, it was found that the two soil classes are identical, and this discrimination was possibly due to acquisition characteristics (such as sensor angle, shadow effects and light incidence). Therefore, these two soils classes were merged into one soil class, as illustrated in Figure 10
The seasonality of vegetation, which is directly related to the season of the year, associated with the efficiency of the classifier, allowed distinguishing areas of vegetation, in the bottom and at the top of the waste pile. Furthermore, comparing the results presented in Figure 8
, a relationship can be observed between the NDVI, LULC and temperature. The areas classified as vegetation (higher NDVI values) correspond to the areas with a lower temperature in all the images, corroborating the complementation of the analysis and characterization of the LULC.
3.4. Hydrogeochemical Characterization
The hydrogeochemical features of water affected by coal mining (A2, A3 and A4) are clearly distinct from water free from such influence (A1 and A5).
Mining effluents are hard in nature, with EC mean values higher than 800 μS/cm, as a result of the high concentration of inorganic dissolved solids, metals and metalloids (As, Fe, Mn and Al). Fe and Mn, with mean concentrations above 50,000 μg/L and 4000 μg/L, respectively, justify the presence of precipitation deposits in the stream affected by mine drainage. Samples collected in A4 are a mixture of surface water and mining effluents, presenting an EC mean value around 500 μS/cm and concentrations of Fe and Mn around 29,000 μg/L and 2900 μg/L, respectively, also far above the legal limits for domestic and agricultural purposes [62
Samples without the influence of mine drainage (A1 and A5) have EC below 90 μS/cm, significantly lower than the values recorded in the polluted waters.
EC and SO4
are good indicators of pollution by coal mine effluents and are normally closely related since EC is very sensitive to the presence of sulfate ions. The high concentration of SO4
observed at points A2, A3 and A4 (between 200 and 400 mg/L) contrasts with the concentrations observed in A1 and A5 (below 15 mg/L) and may be related to the oxidation of pyrite and, possibly, arsenopyrite in the rock massif affected by coal mining. EC and SO4
are extremely sensitive to the influence of mine drainage, even when large dilutions occur. Unlike other ions, SO4
is not removed by processes of sorption and precipitation, remaining stable even if water pH varies. This parameter can also be used to predict the approximate concentrations of metals when pH values are within their solubility limits [64
The water pH values, including mine effluents, are relatively constant over time, ranging from 6.0 to 6.5, being classified as neutral to nearly neutral or slightly acidic waters according to Morin and Hutt (2001) (pH between 6 and 9 to 10) [65
The concentrations of Ca, Na and Mg are higher in mining drainage areas than in natural water courses. Their presence is due to the conditions inside the mines that are conducive to water–rock interaction and promoted by the particular chemical characteristics of these waters.
The mine effluents’ hydrogeochemical facies reflect the circulation paths and water–rock interaction processes which take place in the coal mine. Specifically, mine effluents (A2 and A3) present a typical Mg-SO4 facies, and water from the Silveirinhos stream downstream of the mine effluent discharge (A4) presents a Mg-SO4/HCO3 facies. On the other hand, groundwater without mining influence (A5) has a Na-Cl facies, while water upstream of discharges (A1) has an intermediate Na/Mg-SO4
/Cl facies [36
The physicochemical and hydrogeochemical features of the mine effluents observed in A2 and A3 drainage galleries point to deep groundwater circulation in the exploited massif and to significant water–rock interaction. Additionally, the occurrence of groundwater pollution originating from rainwater infiltrating and percolating in the waste pile vadose zone is not clear and is probably residual or even inexistent. Therefore, the main source of water pollution expressed by major ions, PTEs and Fe content seems to be the mine drainage in pores, fractures and galleries of the exploited massif.
The overall quality of groundwater from the drainage galleries (A2 and A3), as well as surface water from the Silveirinhos stream (A4) downstream of the mine effluent discharge sites, can be classified as poor, and these waters should not be supplied for human activities, namely, domestic consumption (e.g., drinking, food preparation, bathing) and agriculture.
This component of the research represents a contribution to the understanding of the regional impact of coal mine drainage on water resources. In order to improve the knowledge on this subject in future studies, new sampling campaigns should be carried out in an extended monitoring network.
3.5. Soil Characterization
The waste pile characterization in areas affected and unaffected by self-burning pointed to differences regarding soil geochemistry, mineralogy, morphological features of the soil profile and unsaturated hydraulic conductivity. Self-burning caused severe changes in the hydropedological setting, originating great contrasts in the leaching susceptibility of PTEs in different zones of the São Pedro da Cova coal mine waste pile [52
]. The study focused on the waste pile identified the most abundant PTEs in the coal wastes as Cr, As and Pb, while Zn and Mn prevailed in the leachates [52
The characterization of soils surrounding the waste pile and the old mine facilities comprehending their geochemical characterization allows the identification and quantification of hazardous soil pollutants in the study area. The results from the two soil geochemical campaigns were identical, with the average concentrations of the trace elements being similar in both (Figure 11
). Nonetheless, from the first campaign, at the end of the rainy season, to the second campaign, at the end of the dry season, there was a generalized trend for soils to maintain or moderately reduce the concentrations of trace elements, with exceptions for Pb and Zn, which suffered a small increase.
The results from the soil sampling campaigns were compared with Portuguese reference values suggested by Agência Portuguesa do Ambiente
(APA—the Portuguese Environmental Agency) for agricultural soils [66
]. The choice of comparing the soil analytical results with agricultural soil reference values was based on the fact that the community that surrounds the mine still practices subsistence farming; therefore, a conservative approach was thought to be more adequate. Figure 12
compares the percentage of contaminated soil samples in both campaigns based on the APA reference values [63
] and shows that several trace elements, particularly Sb, Ba, U, As, V, Be, Cr, Mo, Pb and Se, exceeded the threshold values proposed for soil contamination in agricultural areas of 1.0 mg/kg (Sb), 210.0 mg/kg (Ba), 1.9 mg/kg (U), 11.0 mg/kg (As), 86.0 mg/kg (V), 2.5 mg/kg (Be), 67.0 mg/kg (Cr), 2.0 mg/kg (Mo), 45.0 mg/kg (Pb) and 1.2 mg/kg (Se), respectively, both upstream and downstream of the waste pile, in both campaigns. From the first to the second campaign, the number of contaminated samples tended to decrease, except in the case of Pb and Ta.
Geostatistical algorithms, namely, ordinary kriging, were used to model the concentration differences of trace elements between the two surveys, in the study area. The spatial distribution analysis of the percentage difference on elemental concentrations provides a good insight into the areas that suffered an addition or depletion of some elements over time, contributing a better understanding of the contamination evolution and dynamics.
represents the concentration differences between the first and second soil geochemical surveys, for the trace elements that presented higher contamination rates in the studied samples, namely, Sb, Ba, As, U and V.
Despite the fact that nearly all samples present values above the reference values for these elements, comparing both soil campaigns, Ba and U do not express a significant concentration change. This may be indicative of a stable or slow contamination process.
The concentration difference resulting from the two campaigns shows a generalized trend to maintain or reduce the Sb concentration, mostly in forest areas, with the exception of small scattered areas that registered an increase in concentration. These localized concentrations occurred both in urban areas and forests and were always upstream of the mine waste pile. The randomness of the distribution does not seem to show a clear trend and may be related to isolated soil heterogeneities.
East of the study area, in forest areas mostly upstream of the mine, As tends to maintain its concentration, or it moderately decreases, while to the northwest, an area is highlighted, suffering an increase of over 80% from the first to the second campaign. This area is located in an urban agglomerate, located mostly upstream of the waste pile and its drainage basin. There is no clear influence from the mine waste piles in the As concentration increase. Similarly, Pb, Mo, Cu, Zn, Co, Ni and Ag also show weak to moderate increasing concentration patterns in the urban areas to the west of the study area, consistent with anthropogenic urban and industrial pollution [68
The trace elements V, Be and Cr present a concentration area inside the waste pile drainage basin, located contiguously to the wastes, which can vary between 20 and 40% in the case of Be and between 40 and 60% in the case of Cr and which can reach 80% in the case of V. The elements Cr and Se show another anomaly, with concentration increases higher than 80%, inside the drainage basin, located approximately 350 m downstream of the waste pile. The elements V, Be and Cr are present in significant quantities in the deposited wastes [70
], pointing to an accumulation caused by mine leaching. The increasing concentrations trends of Se can be comparable with patterns observed in the south of the study area by Cr, with V showing a similar spatial distribution.
The contamination rates for Ta are relatively low, as only 6% and 16% of the total soil samples exceeded the threshold proposed for agricultural soils [67
] in the successive surveys; however, they registered an increase in the number of contaminated samples with time. Spatially, the Ta concentration increase is placed on the eastern limit of the waste pile drainage basin, extending to a forest area that is locally affected by small waste mine deposits, pointing to an accumulation potentiated by leaching from the mine wastes, as Ta is an element present in these wastes and frequently present in coals [70
The cadmium concentration in the sampled soils is very low, and none of the soil samples exceeded the threshold reference values proposed for agricultural soils.
The different approaches to soil characterization allowed not only describing the material deposited on the waste pile and defining its contamination potential but also monitoring the soils around the mine, classifying their contamination rates and evaluating preferential areas of concentration or depletion of pollutants. Despite the fact that Ba, U, As and Sb show significant contamination rates [67
], this approach does not show a spatial relation between the areas that suffered a concentration increase and the mine wastes. On the contrary, V, Be, Cr and Ta may be suffering concentration increases as the result of pollutant transportation from the mine wastes.
3.6. GIS Data Integration
The PostGIS 3.0 relational database is an extension of PostgreSQL 12.1 [73
]. It was used to store the information acquired in the field. The database includes data from water and soil analysis, metadata of the raster files, temperature measurements (points and raster files), elevation information (DEM) and LULC. The database was connected to the developed GIS application. The Coal Mine application was created using the Python programming language, QGIS application programming interface (API) and Qt API methods [55
]. It consists of a button that opens the main window composed of seven menus: File, Water, Soil, Temperature Points, Temperature Maps, Digital Elevation Model (DEM) and Land Use Land Cover (LULC). Basic and standard tools such as Zoom in/out and Pan, a table of contents (Layers) and the canvas complemented the application [55
presents the graphical interface of Coal Mine with overlapped information related to the thermic orthomosaic, the contour layer from 2019, the contour layer from 2020 and the points with the altimetric variations. Besides the menus to open the different layers, a table of contents is provided with an overlapping order (column on the left).
Each menu connects automatically to the database (where the data are hosted), filters the request and presents the results in the canvas. Different options are provided in each menu: (i) the possibility to open a specific campaign by date, in soil and water campaigns, for instance; (ii) provides interpolation algorithms such as kriging or inverse distance weighting (IDW) to create continuous surfaces, e.g., in temperature measurements; (iii) allows opening raster files (such as DEM) already created; (iv) allows creating slope and aspect maps (from DEM); (v) provides the possibility to open an LULC already created or to apply the k-means unsupervised classification method [52
]. The database is continuously updated with new information. This is one of the greatest advantages of being connected to the PostGIS database. When the database is updated, the information is automatically incorporated into the Coal Mine application. The data from campaigns relative to water, soils and temperature were incorporated in the GIS application so the user can access the information and compare the data regarding the different campaigns. Additionally, the DEMs created from UAV campaigns were incorporated, as well as the LULC maps. The GIS application is dynamic and allows users to easily access the data in the form of dynamic maps to combine them. The application code is hosted in GitHub, and it is available at https://github.com/liaduarte/Coal-Mine-Project.git
(accessed on 30 April 2021).
3.7. Contribution of an Integrated Multi-Approach for Environmental Monitoring
An abandoned mine is a complex system, with multiple variables, that can bring risks to the local community, affecting the environment and human health. It can change the landscape, producing wastes that concentrate PTEs and likely disseminate them in the surroundings, causing water and soil pollution [3
]. In addition, the presence of underground works and waste piles may contribute to ground subsidence.
In the case of the São Pedro da Cova mine, there is a higher level of complexity, since the mine’s waste pile has been self-burning since 2005, which can influence its physical stability, as well as the chemical composition and concentration of the contaminants present [25
], and, consequently, its migration and dissemination capability [32
]. This complexity necessitates an integrative approach gathering different methodologies that would be able to contemplate multiple variables regarding abandoned mine monitoring. The developed Coal Mine GIS open-source application is the tool that permits monitoring of environmental parameters, allowing the creation of risks maps representing their spatial and temporal variations [24
The self-burning has been occurring since 2005 and nothing has ever been done to extinguish the combustion. Even though the combustion seems less intense since 2013, the long-term environmental impacts are of significant concern, principally those related to emissions of gaseous organic compounds and particulate matter to the atmosphere, and with the leaching of PTEs to the surrounding areas (affecting soils and waters). As a consequence of the combustion of the coaly waste materials, the mode of occurrence of some elements can change and concentrate due to the loss of organic matter, due to its solubilization or volatilization. The results obtained in this integrative approach allow the comprehension of the combustion process and the identification of potential evolutive scenarios in the São Pedro da Cova waste pile, since a significant part of the waste pile was not affected by the self-burning. Only with this information will it be possible to define the proper mitigation and prevention actions by decision-makers.
The integrated multi-approach of the studied environmental parameters for the São Pedro da Cova waste pile monitoring was performed considering both the evaluation of the dynamics of the self-burning, its effects on this complex structure and the consequences in the surrounding areas, namely, in soils and waters [36
]. This is particularly relevant because, as mentioned before, the São Pedro da Cova waste pile is located in a populated area. For this purpose, remote sensing using a UAV, together with the traditional methodologies, proved to be a successful approach for the collection of the information for environmental assessment and monitoring.
Furthermore, the data generated within this project are being stored and manipulated in a relational database that is continuously updated with new information [52
]. This process facilitates data management as the updates are easier to conduct and provide immediate comparisons with related data from previous campaigns. This integrated multi-approach provides a global understanding of the effects of abandoned mines on the surrounding environment, allowing the analysis of each monitored parameters’ evolution with time, which provides a good idea of the current dynamics of the different processes involved.