Spatial analysis of the mining and transport of rock minerals (aggregates) in the context of regional development

The first analysis concerns the density of aggregates production and changes in time. It has been performed using the Epanechnikov quadratic Kernel function (McCoy et al. 2004). Kernel is a weighting function used in non-parametric estimation such as density estimation to calculate a variable density function (in this case rock raw material production). The general function is given by the following formula (Epanechnikov 1969):where K ₀ is the Kernel function, λ is the bandwidth (smoothing parameter), which determines the width of neighbourhood and degree of smoothness.

Quadratic kernel function has the form of

The result of applying the Kernel density function in GIS is a smoothly curved surface that is fitted over each point representing magnitude per unit area of the analysed variable (McCoy et al. 2004).

The input parameters include: spatial location, population field value, and the search radius. The point locations of all quarries (300) and their annual output, as the population field value (in tonnes), have been used as input data for this analysis. The surfaces representing the density of aggregates production per unit area (km²) have been calculated for the years 2006 to 2010. The pixel size of each output raster surface is 100 × 100 m and a search radius of 20,000 m has been used. The results are shown and discussed in "Density of rock minerals production".

The principal originating locations of the road transport of aggregates are quarries and processing plants. In this analysis, the location and size of these origins have been calculated based on their daily and annual production; the information comes from the regional mining authority and surveys covering the types of transport used by the quarries (rail, road, or mixed). The size of road transport originating locations has been expressed as the daily and annual number of trucks necessary to carry aggregates from the originating locations (quarries) to receiver areas (construction sites). The quarries have been differentiated on the basis of the type of transport used: road, rail and mixed.

As a first step, for the quarries using road transport only, daily production has been calculated, dividing the annual output by the number of working days in a given year. Next, the number of trucks has been calculated, dividing daily production by the standard capacity of a haulage truck used to transport aggregates. In the case of quarries using mixed transport, a known fraction of the output has been used to calculate the number of trucks required. In the case of mines using rail transport, a fraction representing 10 % of the output has been used to account for the local road transport of aggregates.

Furthermore, the quarries with rail sidings on premises and quarries transporting rock minerals to a nearby loading point have been differentiated. The numbers of trucks, calculated by the above process, have been used as the population field to determine the magnitude per unit area of road transport using the Kernel density function. The results are presented and discussed in “Scale of road transport flows for aggregates”.

In another work, the potential of the railway network to transport aggregates has been studied. The adopted methodology identified quarries that use road transport in a search radius of up to 5 km (at a 1 km interval) from the railway loading stations, using a spatial analysis buffering function. This function is commonly used for proximity analysis and creates buffer zones around selected objects at a specified radius or radii in case of multiple buffers. Next, statistics have been calculated for each railway loading station including its potential capacity and the total output from the identified quarries in its neighbourhood. In the same way, the potential of closed railway lines to carry aggregates has been estimated. The results of this analysis are described in “Potential of railways to transport aggregates”.

The next analysis estimated how long, in years, the mineral reserves of active quarrying operations will last at a given output level. For this purpose, the mean annual production of each quarry has been calculated based on average outputs for the past 5 years (2006–2010). Once this value has been obtained, several scenarios of aggregates demand have been analysed, ranging from a 20 % increase to a 30 % decrease in production. The following formula has been used.where S _m is a size of mineral reserves of m mine, R _m is a economic reserves—technically and economically extractable part of economic resources (PGI 2011) of m mine, P _m is a averaged annual production of m mine, k parameter representing annual production scenarios in the range 0.7–1.2.

The reserve sufficiency of a given mineral type (e.g., granite) has been calculated taking into account the total economic reserves that are the technically and economically extractable parts of economic resources according to a national classification (PGI 2011) or category 221 according to the UNCF-2009 classification (UN 2010); and the total averaged production of the mineral using the above equation. Calculations have been done in ArcGIS using a table field calculator and Visual Basic Script functions. The results are shown and described in “Sufficiency of aggregates reserves”.

Valorisation can be thought of as assigning values to objects, e.g., an undeveloped mineral deposit, based on selected criteria. In a subsidiary study, an analysis has been conducted to obtain a classification of undeveloped aggregates deposits, characterising their suitability for development, using a GIS cartographic model based on a modification of the valorisation concept proposed by Radwanek-Bąk (2007) and Nieć and Radwanek-Bąk (2011). The modified method includes the following ranking criteria: environmental and land use constraints, the quality and quantity of geological reserves, and the transport accessibility of the deposit. The environmental constraints include location within or in the proximity to nature protection areas, forests, or underground water reservoirs. The land-use constraints include: arable land, orchards, and developed areas (e.g., settlements). The transport criterion focuses on proximity to railway network. In contrast to Nieć and Radwanek-Bąk (2011), the deposits located in the proximity of a railway system have been assigned higher scores than a road system as this form of transport is preferred.

This analysis results in four values: environmental score, land-use score, geological reserves (taking into account potentials of other sources of a given mineral in the country) score, and transport option score. Based on these values, four symbols have been assigned to each deposit in a manner similar to the one proposed by Nieć and Radwanek-Bąk (2011). The scores have been calculated using cartographic modelling procedures developed in GIS. This facilitates and streamlines the otherwise time consuming and tedious operations. The cartographic modelling technique is explained through the example of calculating the environmental score. This consists of a set of subsequent spatial operations that, first, determines if undeveloped deposits coincide with various forms of nature protection areas (i.e., nature reserves, landscape parks, areas of protected landscape), underground water reservoirs, and forests. Based on the type of coincidence, expressed in terms of percentage of overlapping areas, three sub-scores are calculated. Depending on the value of the sum of these subscores (2–6), the magnitude of the conflict is determined in terms of environmental protection requirements; thus a symbol representing an adequate category is assigned. The possible categories are: deposits unavailable for development (C), deposits conditionally available (B), and deposits accessible without restrictions (A). The criteria proposed in this part of valorisation excluded deposits within the boundaries of protected areas and protected underground water reservoirs (C) thus are more conservative than those proposed by the abovementioned authors. In the case of forests and arable land, the scores were assigned based on the fraction of the deposit area falling within these areas (30, 31–90, and over 90 %). Deposits coinciding with orchards or built-up areas were excluded and assigned the C category (unavailable for development). The diagram showing part of the modelling procedure used to determine the sub-score for nature protection areas is shown in Fig. 1.

To calculate the geological reserve score, the proven quantity of mineral in the deposit and its uniqueness (existence of other documented deposits) has been used. The larger the deposit, the higher the score that has been given. The transport score used the distance from railway lines and railway loading points (up to 5 km away). The results are shown and described in (Valorisation of aggregates deposits).

The maps of rock mineral production density between 2006 and 2010 are shown in Fig. 2 (Blachowski 2011). The maps show that the most intensive quarrying takes place in the southern, south-eastern, and central part of the Lower Silesia region. This is determined by regional geology and the consequent location of exploitable deposits. The growth of mineral output represented by more intensive (darker) colours in successive years (2006–2010) is related to the rise in production from the region’s quarries; the overall regional increase was 70 % or 12,532 thousand tonnes in the case of dimension and crushed stones and 21 % or 2,510 thousand tonnes for sand and gravels.

Analysing the density of production maps in more detail, a slow shift of the highest production zones from the southern and central parts in an easterly direction can be observed between 2006 and 2010. This may be attributed to increased output from mines located in the south-eastern and the eastern parts of the Lower Silesia, as well as opening up new mining operations in these areas.

The proposed methodology of analysing the magnitude of road traffic generated by transport of aggregates, described in “Scale of road transport flows for aggregates”, has been developed to overcome the problem of the lack of information on the number of aggregates haulage trucks in periodic surveys of road traffic.

Analysis of the source data shows that out of the total number of 300 operating quarries, 213 use trucks only, 39 use railway but need to transport rock minerals to a nearby loading point using trucks, 17 use railway only, and 1 quarry uses rail, with a conveyor belt to the railway loading point system.

The spatial distribution of the origins of aggregates transported by road has been shown as graduated symbols in the maps in Figs. 3 and 4. The first map shows quarries using road transport only; the second one shows quarries using both rail and road transport. The size of the symbol on the map represents the magnitude of the source in trucks per day. In the analysed year (2010), there were 17 quarries needing more than 100 trucks per day and 28 requiring more than 50. The largest operations generate traffic on public roads of between 200 and 500 trucks (this does not take into account the return trip). The highest concentration of origins of relevant road traffic flows exists in the central part of the Lower Silesia region. These transport flows begin on local roads and are then taken onto regional and national roads that are used to move aggregates to delivery sites. This has been shown in Fig. 5 on a map representing the number of trucks per unit area (km²) needed to transport aggregates from the quarries using road transport only. These identified zones of intensive road transport are located in the eastern and central parts of the region where longitudinal and latitudinal transport belts can be distinguished. These are congruous with the zones of quarry production (Fig. 2).

Analysis of the potential for railways to transport aggregates shows that there are presently 69 quarries using road transport only within the radius of up to 5 km from the 56 existing railway loading points. In 2010, 51 of these mines were in operation. Their output amounted to 1/3 of the total production of quarries using road transport only, which corresponds to approximately 1,000 trucks per day (Table 2). This load could theoretically be moved using the rail transport option.

It has to be taken into account that this analysis concentrates on spatial aspects and does not take into account economic factors in the road and rail transport of aggregates. However, conversion to a mixed form of transport or change to conveyor belt transport systems would significantly reduce the impact on road traffic for even the nearest of quarrying operations.

In this paper, the scenario of maintaining current production averaged from data for the last 5 years has been presented. The analysis has been carried out separately for dimension and crushed stones, sands and gravels, and clay minerals. Figure 6 shows the estimated availability of economic reserves in place (industrial resources) or category 221 resources (UNCF-2009 classification) for already developed deposits; this is given in years of exploitation and their spatial location in the region.

The results indicate the available reserves of important magmatic and metamorphic rock minerals: granites, gabbro, and melaphyres, exceed 100 years; reserves of basalts, gneisses, and migmatites exceed 50 years; while amphibolites and serpentinites will last for at least 20 years at the present level of production (Fig. 7).

In the case of the 67 operating sand and gravel pits, 8 have reserves for more than 50 years, 9 for over 25 years, 18 between 11 and 25 years, and 32 for up to 10 years (Fig. 8).

These results give an approximate estimation of the available reserves without the need to open up new quarrying operations. The expected production life of mine may be extended if the output decreases with time or additional reserves are found.

The spatial development and management of an area may restrict or exclude from potential future exploitation some undeveloped but identified deposits. Valorisation provides a measure of the value of deposits and the basis for protection of the most valuable ones and withdrawal from protection of the least valued. This should result in a balance between the need for other forms of spatial development and preservation of rock mineral deposits for future use. Information on the availability of reserves from operating mines (part 4.4) provides an additional criterion to be taken into account.

Applying the analytical cartographic model (part 3.5), 68 undeveloped deposits in the Lower Silesia region have been selected and each one has been assigned a four symbol score representing their suitability for future development in terms of: the environmental constraints, availability of transport networks, current land use, and quantity and quality of mineral reserves. This score together with detailed characteristics of each deposit is important information for regional authorities in the process of granting licenses to extract minerals from deposits.

The results of this analysis show that future exploitation of many identified aggregates deposits may be prohibited—category C (25 deposits) or limited—category B (40) mainly because of environmental protection considerations. The remaining ones (13) have been assigned category A.