Index
A
Activated carbon (AC), xv, 165–167, 175, 177
Administrative units, 31, 33, 38, 39
Agenda 2030, 105, 106, 115
Agent based modelling, vii
Aggregation
aggregative-compensative approach, 23
data matrix, 159
functionality, 8, 10
happiness index, 209
Hasse diagram, 26
mathematical problem, vii–viii
multiple indicators, xii
Aggregative-compensatory approach, 23, 24,
229, 248, 251
Algebra, x–xii, 27, 222
Average height, 68–69, 154, 156, 160, 162,
171, 191, 207, 224, 226, 228
B
Bacillus licheniformis, 197, 199
Basis partial ordering, 206–207
Berliner Wasserbetriebe (BWB), 105–107,
109, 110, 115
Biomonitoring
lichen, 64
metals and metalloids, 64
MIS, 63
multi-indicator systems, 77
“naturality/alteration scales”, 77
Brine
chlorine, 131
dependent indicators, 121, 122
environmental performance, 131
inland desalination plants, 128, 130
retrospective validation, 130
temperature, brine discharge, 119
Bruggemann and Kerber 2018 (BK) theory,
91–93, 98, 100
Brunauer, Emmett and Teller (BET), 166
Bubley-Dyer approach, 154, 162, 163
C
Carbonization, 166, 167, 177
Carbonization-activation reaction, 166
Chemical activation, 166
Child well-being, 46, 56, 57, 60
Chlorine, 98, 122, 130, 131
Civil engineering services, xv, 106, 107, 115,
116
Clustering, 32, 33, 36, 220
See also Reverse clustering
Combinatorics, x, 24, 26
Complex Adaptive System (CAS), 20, 21
Complexity
CAS, 20
“complexity preserving”, 229
complex systems, 20
concept of “system”, 18
and dimensionality, 21, 239
environmental systems, 63
Hasse diagrams, 26
indicators role, 18
LSI indicator, 127
MCDA, 109
multidimensionality and ordinality, 235
© The Editor(s) (if applicable) and The Author(s), under exclusive license to
Springer Nature Switzerland AG 2021
R. Bruggemann et al. (eds.), Measuring and Understanding Complex Phenomena,
https://doi.org/10.1007/978-3-030-59683-5
309
A
Activated carbon (AC), xv, 165–167, 175, 177
Administrative units, 31, 33, 38, 39
Agenda 2030, 105, 106, 115
Agent based modelling, vii
Aggregation
aggregative-compensative approach, 23
data matrix, 159
functionality, 8, 10
happiness index, 209
Hasse diagram, 26
mathematical problem, vii–viii
multiple indicators, xii
Aggregative-compensatory approach, 23, 24,
229, 248, 251
Algebra, x–xii, 27, 222
Average height, 68–69, 154, 156, 160, 162,
171, 191, 207, 224, 226, 228
B
Bacillus licheniformis, 197, 199
Basis partial ordering, 206–207
Berliner Wasserbetriebe (BWB), 105–107,
109, 110, 115
Biomonitoring
lichen, 64
metals and metalloids, 64
MIS, 63
multi-indicator systems, 77
“naturality/alteration scales”, 77
Brine
chlorine, 131
dependent indicators, 121, 122
environmental performance, 131
inland desalination plants, 128, 130
retrospective validation, 130
temperature, brine discharge, 119
Bruggemann and Kerber 2018 (BK) theory,
91–93, 98, 100
Brunauer, Emmett and Teller (BET), 166
Bubley-Dyer approach, 154, 162, 163
C
Carbonization, 166, 167, 177
Carbonization-activation reaction, 166
Chemical activation, 166
Child well-being, 46, 56, 57, 60
Chlorine, 98, 122, 130, 131
Civil engineering services, xv, 106, 107, 115,
116
Clustering, 32, 33, 36, 220
See also Reverse clustering
Combinatorics, x, 24, 26
Complex Adaptive System (CAS), 20, 21
Complexity
CAS, 20
“complexity preserving”, 229
complex systems, 20
concept of “system”, 18
and dimensionality, 21, 239
environmental systems, 63
Hasse diagrams, 26
indicators role, 18
LSI indicator, 127
MCDA, 109
multidimensionality and ordinality, 235
© The Editor(s) (if applicable) and The Author(s), under exclusive license to
Springer Nature Switzerland AG 2021
R. Bruggemann et al. (eds.), Measuring and Understanding Complex Phenomena,
https://doi.org/10.1007/978-3-030-59683-5
309