2.3 EEQE system construction
Figure 2 exhibits the procedures of constructing an EEQE system of mining area. Indicators that can reflect the eco-environment levels of coal mining area are selected and then weighed using genetic projection pursuit model. After evaluating and mapping regional ecological environment, the spatial autocorrelation and driving factors are analysed to guide localised environmental protection and mine design.
2.3.1 Evaluation indicator selection
Indicators for assessing eco-environment levels of a coal mine are rather complicated, and an appropriate selection is critical for EEQE. In accordance with the principle of evaluation indictor system construction and taking practicality and accessibility into account, 13 indicators are selected in regard to geomorphology, climate, hydrology, land resource, vegetation and human activity factors.
Geomorphology is closely related to hydrology, soil, vegetation and creature; its impact on ecological environment is characterised using elevation, terrain slope, terrain aspect and geomorphic type 18. Annual average precipitation and evaporation determine localised climatic conditions, thus making it a nature that the area with adequate rainfall sees greater vegetation coverage. In contrast, the greater the evaporation, the lower the moisture content of surface soils, possibly leading to water loss and land salinisation 19. Shallow aquifer is a key hydrological factor underpinning ecosystem stability and characterised by specific yield; the greater the specific yield, the stronger the ability of an aquifer in water release 20. River system distance represents the situation of surface waters, reflecting the impact of flows on surrounding soil erosion 21 and on the circumstance of flora and fauna community 22. As a factor affecting water and soil conservation as well as the stability of eco-system, vegetation coverage is represented via normalized differential vegetation index (NDVI) 23. Also, land resource utilisation and layout are considered because of their eco-environmental impact; for example, regional ecology of the land for construction can be damaged to a great extent 24.
Table 2
Positive external indicators of large-scale underground longwall mining
Indictors | Parameter | Indictors | Parameter |
Coal seam thickness | ≥3.5 m | Mine output | 500~1000Mt/a, or ≥1000 Mt/a |
Panel width | ≥200m | Ratio of depth to thickness | H/M<100 |
Retreat rate | ≥5m/d | | |
It is generally believed that the impact of underground coal mining on ecological environment is positively correlated to mining intensity 25. The indicators representing mining intensity include positive external indicators (listed in Table 2) and negative external indicators 26. The negative ones refer to the consequences resulting from coal seam extraction, which include overburden strata movement and eco-environmental damage. According to the classification standard, the previously mined area of Ili No.4 Coal Mine can be classified into high-intensity mining area. In contrast, the coal reserve area of Ili No.4 Coal Mine and the planned survey area of Ili No.5 Coal Mine should be unmined area. Within the whole area of the case, surface subsidence induced by coal exploitation can decrease available land resource, accelerate soil erosion, alter runoff and catchment conditions, and deteriorate ecological environment. Figure 3 shows the data map of the 13 indicators.
2.3.2 Indicator weight determination
The weight of 13 indicators is calculated using genetic projection pursuit model. For this purpose, high dimensional data are projected onto a lower dimensional space to construct objective functions and identify the best projection path capable of reflecting the structural feature of high dimensional data 27,28. The basic procedures for projection pursuit modelling are as follows:
(1)Data standardisation
The indicators are different in dimension and order of magnitude, which makes them lack comparability. So, the indictors are standardised by means of polarisation method in ArcGIS analytical tools. The processed indicators are between 0 and 1.
(2)Projection indicator function construction
The sample set is \(\left\{ {x(i,j)\left| {i=1,2} \right.} \right. \cdot \cdot \cdot ,n;\left. {j=1,2 \cdot \cdot \cdot ,m} \right\}\), where m refers to the number of evaluation indicators and n is the number of samples. The one-dimensional projection (\(Vi\)) of m-dimensional data along the direction \(c=\left\{ {c(1),c(2)} \right.,c(3), \cdot \cdot \cdot ,\left. {c(m)} \right\}\) is expressed as:
\(Vi=\sum\limits_{{j=1}}^{m} {cj} \cdot x(i,j),i=1,2 \cdot \cdot \cdot ,n\) (Eq. 1)
To meet two requirements that (i) local projection points should aggregate to the greatest extent and (ii) overall the projection should disperse as much as possible, a projection indicator function is established:
\(Q(c)=S(c) \bullet D(c)\) (Eq. 2)
\(S(c)=\sqrt {\frac{{\sum\limits_{{i=1}}^{n} {{{(Vi - E(Vi))}^2}} }}{{n - 1}}}\) (Eq. 3)
\(D(c)=\sum\limits_{{i=1}}^{n} {\sum\limits_{{j=1}}^{n} {[R - rij]} } \bullet f[R - rij]\) (Eq. 4)
Where \(S(c)\) is inter-class distance, \(D(c)\) is within-class density, \(E(Vi)\) is the mean of \(\left\{ {Vi} \right.\left| {i=} \right.1,\left. {2 \cdot \cdot \cdot ,n} \right\}\), \(rij\) is inter-sample distance and \(rij=\left| {Vi} \right. - \left. {Vj} \right|\), R is the window radius of local density, and \(f[R - rij]\) is step function where if R is greater than \(rij\), \(f[R - rij]\) equals 1 but if not, \(f[R - rij]\) equals 0. \(D(c)\) represents the aggregation level of projection points; much greater the value of \(D(c)\), more aggregated the points.
(3)Projection indicator function optimisation
Projection directions (c) reflect the structural features of data. The maximised objective function and the corresponding constraint condition are expressed as:
\(Max:Q(c)=S(c) \bullet D(c)\) (Eq. 5)
\(s.t.\sum\limits_{{j=1}}^{m} {c{j^2}} =1\) (Eq. 6)
Considering that the calculation of the best projection direction is a complicated nonlinear optimisation problem, genetic algorithm is employed to identify the optimal projection direction.
2.3.3 EEQE modelling
Based on the optimal projection direction vector obtained above, the eco-environmental quality of coal mining area is quantified using mining area eco-environmental quality indicator (MAEEQI), in which the direction vector is as the weight of each evaluation indicator. Then the weighted summation of all indicators is calculated:
\(MAEEQI=\sum\limits_{{i=1}}^{n} {wi \cdot ui} =w1u1+w2u2+ \cdot \cdot \cdot +w2u2\) (Eq. 7)
\(ui=c{j^2}\) (Eq. 8)
Where ui represents the weight of each indicator, wi is the standardised value of each indictor, and n is the number of evaluation indicators.
2.3.4 Spatial autocorrelation
As an approach to analyse the distribution characteristics of data, spatial autocorrelation is useful for testing the significance of an attribute value of variables and verifying the relevance of attributes between adjacent points. In this paper, spatial autocorrelation analysis is used to study the aggregation characteristics of the eco-environment conditions in Ibei Coalfield 29,30.
(1)Global autocorrelation
Global autocorrelation characterises the aggregation and dispersion degree of eco-environmental quality within the whole space and expressed using Global Moran’s I ranging between -1 and 1. There is:
\(I=\frac{{n\sum\limits_{{i=1}}^{n} {\sum\limits_{{j=1}}^{n} {wij(xi - \overline {x} )(xj - \overline {x} )} } }}{{\sum\limits_{{i=1}}^{n} {\sum\limits_{{j=1}}^{n} {wij{{(xi - \overline {x} )}^2}} } }}\) (Eq. 9)
Where I is the indictor of global autocorrelation, n is the total amount of elements, xi and xj are the eco-environmental quality level of spatial unit i and j respectively, \(\overline {x}\) is the average value of eco-environmental quality, and wij represents spatial weight coefficient matrix 31.
(2)Local autocorrelation
Further, local spatial autocorrelation (expressed as Local Moran’s I, ranging from -1 to 1) can be employed to analyse the aggregation and dispersion of eco-environmental quality in localised area. There is:
\(Ip=\frac{{n(xi - \overline {x} )\sum\limits_{{j=1}}^{n} {wij(xj - \overline {x} )} }}{{\sum\limits_{{i=1}}^{n} {{{(xi - \overline {x} )}^2}} }}\) (Eq. 10)
2.3.5 Geographic detector
As a statistical method to analyse the spatial heterogeneity of data, geographic detector can identify the causality of different elements within a localised scale. The advantage is that this method can not only detect both quantitative and qualitative data, but also determine the interactive effect of two factors on dependent variable, even without any beforehand assumptions and constraint conditions 32,33.
(1)Factor detection
Factor detection is employed to identify the spatial heterogeneity of eco-environmental quality in Ibei Coalfield and to analyse the impact degree of various indictor factors (X) on eco-environmental quality (Y). The result is measured using q value, which can be expressed as:
\(q=1 - \frac{{\sum\limits_{{h=1}}^{L} {Nh\sigma {h^2}} }}{{N{\sigma ^2}}}=1 - \frac{{SSW}}{{SST}}\) (Eq. 11)
Where L is the layer of variable Y or factor X, Nh is the number of units in the hth layer, N is the number of units in the whole area, \(\sigma {h^2}\) is the variance of Y in the hth layer, \({\sigma ^2}\) is the variance of Y in the whole area, and \(SSW\) and \(SST\) represent the sum of variance of one layer and the whole area, respectively. Figure 4 shows the principle of factor detection.
(2)Interactive effect detection
Interactive effect can assess whether the cooperation of evaluation indicators X1 and X2 can enhance or weaken the interpretation of a single indictor on eco-environmental quality. q that is corresponding to a single indictor is calculated, and so does q under the interaction of two indicators. Then comparisons are conducted among q, \(q(X1 \cap X2)\) and sum of q, and the results are divided into five categories. The types of interactions are shown in Table 3.
Table 3
Classification of interaction type
Criteria | Interaction type |
\(q(X1 \cap X2)cript>\) | Nonlinear attenuation |
\(q(X1 \cap X2)>\hbox{max} (q(X1),q(X2))\) | Bilinear enhancement |
\(\hbox{min} (q(X1),q(X2))cript>\) | Single-linear attenuation |
\(q(X1 \cap X2)=q(X1)+q(X2)\) | Mutual independence |
\(q(X1 \cap X2)>q(X1)+q(X2)\) | Nonlinear enhancement |
(3)Risk detection |
Risk detection is to estimate whether the attribute mean value of two subareas have significant difference, expressed as t statistics.
\(t(\overline {y} h=1 - \overline {y} h=2)=\frac{{\overline {Y} h=1 - \overline {Y} h=2}}{{{{[\frac{{Var(\overline {Y} h=1)}}{{nh=1}}+\frac{{Var(\overline {Y} h=2)}}{{nh=2}}]}^{1/2}}}}\) (Eq. 12)
Where \(\overline {Y} h\) is the attribute mean value of area h, is the number of samples in the area, and \(Var\) represents variance.