For the successful remediation of polluted sites, priority setting among the possible remediation technologies is of major importance. The related decisions are typically conditioned by a limited set of alternative remediation techniques and multiple, often contradicting criteria. These characteristics make the decision problem suitable for applying a formalised discrete multi-criteria decision analysis (MCDA). This paper reports on the outcome of a systematic review of articles published between 1995 and 2020 encompassing 43 MCDA applications to support the selection of the remediation technique for polluted soils. It focuses on the comparison between implementations of the MCDA methodology. The review identifies four gaps where progress can be made to mobilise the full strength of the MCDA methodology to support the remediation of polluted soils: (i) early stakeholder engagement, (ii) inclusion of social criteria, (iii) an informed choice of the weighting and aggregation method, (iv) and sensitivity analysis.

Afforestation of certain areas of a river catchment can reduce the outflow of sediment from that catchment. We developed algorithms and software, called CAMF, to minimize the sediment outflow, based on a) a model for local sediment production, b) some parameters such as retention capacity and saturation threshold, c) a raster geo-database containing elevation data and land use. The software can also be adapted to model the effect of other actions than afforestation. We implemented both Single Flow Direction (SFD) and Multiple Flow Direction (MFD) methods to simulate flow transport. We analyze the differences between the two approaches. With the use of MFD methods the spatial interaction increases. As a consequence, the flow simulation with CAMF-MFD, executed in each iteration of the minimization procedure, has a substantially higher computational cost. The total execution time of CAMF can be prohibitively expensive for large geo-databases, since in each iteration only the cell(s) with the maximum sediment outflow reduction are selected.In each iteration of the minimization procedure, a sediment flow simulation is performed for each candidate cell. Since these simulations are independent of each other, we parallelized CAMF for multi-core processors using Open Multi-Processing (OpenMP) directives. Each thread executes the simulations for a subset of the candidate cells. To distribute the simulations over threads, dynamic scheduling is used to handle the imbalance due to the varying execution time of the simulations for each candidate cell. We also adapted the algorithm in two ways to accelerate the execution. First, in each iteration several cells, that produce nearly the same sediment yield reduction at the outlet, are selected. A threshold T determines the number of selected cells. Second, a complete ranking of all cells, with respect to their potential for sediment yield reduction by afforestation, is only computed every K iterations, while in intermediate iterations only N cells are ranked, namely those at the top of the previous complete ranking. The values for T, K and N substantially reduce the computational cost, while the solution quality is typically only slightly lower.We evaluated the performance of the accelerated variant for minimizing sediment outflow by afforestation using a raster geo-database of the Tabacay catchment (Ecuador), with a cell size of 30m × 30m. From a total of 73 471 non-null cells, 27 246 are candidate cells for afforestation.A high speedup is obtained for up to 28 cores (≈22), leading to a substantial reduction of the execution time. The accelerated variant produces nearly the same yield reduction at the outlet and selects almost the same cells than the original CAMF-MFD. The difference between the set of cells selected by both algorithms is measured by the relative spatial coincidence RSC. Results show that in all considered cases RSC > 99%.

Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima Prefecture, Japan, to identify 1000 cells within decontaminated zones where the then immediate afforestation would have maximally reduced both sediment and residual ¹³⁷Cs export. The 1000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and ¹³⁷Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.

Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima prefecture, Japan, to identify 1,000 cells within decontaminated zones where immediate afforestation would have maximally reduced both sediment and residual ¹³⁷Cs export. The 1,000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and ¹³⁷Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.