Determining rural areas vulnerable to illegal dumping using GIS techniques. Case study: Neamt county, Romania

The paper aims to mapping the potential vulnerable areas to illegal dumping of household waste from rural areas in the extra- Carpathian region of Neamt County. These areas are ordinary in the proximity of built-up areas and buffers areas of 1 km were delimited for every locality. Based on various map layers in vector formats (land use, rivers, built-up areas, roads etc) an assessment method is performed to highlight the potential areas vulnerable to illegal dumping inside these buffer areas at local scale. The results are correlated to field observations and current situation of waste management systems. The maps outline local disparities due to various geographical conditions of county. This approach is a necessary tool in EIA studies particularly for rural waste management systems at local and regional scale which are less studied in current literature than urban areas.

💡 Research Summary

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The paper presents a GIS‑based methodology for identifying and ranking rural areas that are vulnerable to illegal household waste dumping, using the extra‑Carpathian part of Neamț County, Romania, as a case study. The authors begin by noting that, despite EU‑mandated closure of rural dumpsites in 2009 and the extension of waste‑collection services, illegal dumping remains a serious environmental problem in Romanian countryside. To address this gap, they construct a spatial model that integrates multiple vector layers (land‑use, hydrography, roads, administrative boundaries) and a digital elevation model (SRTM).

For each village, three concentric buffers (250 m, 500 m, 1 km) are generated. Within these buffers, seven “susceptible factors” are identified: rivers/creeks, floodplains, pastures, degraded lands (landslides, gullies), old loam sites, local roads, and forest edges. Each factor receives a binary value (1 if present inside the buffer, 0 otherwise). Altitude and slope, derived from the DEM, are classified into low‑altitude/low‑slope categories that receive higher scores because they correspond to areas of higher human settlement density and easier access.

The model combines two groups of scores: restrictive factors (altitude, slope, distance from built‑up area) and susceptible factors. A weighting scheme (approximately 30 % restrictive, 70 % susceptible) is applied, producing a vulnerability index ranging from 5 to 39. The index is then divided into seven classes: very low (5‑10), low (11‑15), insignificant (16‑20), moderate (21‑25), significant (26‑30), high (31‑35), and very high (>35).

Validation is performed using field surveys conducted between September 2009 and 2011, together with Google Earth imagery. A total of 163 illegal dumping sites were mapped; 159 (97.5 %) fell within the 1 km buffer, confirming the model’s predictive power. The highest vulnerability classes are concentrated along riverbanks and floodplains, especially within the 250 m and 500 m buffers, whereas high‑altitude, steep‑slope zones in the county’s hilly and mountainous parts show low vulnerability. The authors also note that degraded lands and old loam sites, while less common, become dumping spots when they are easily reachable from local roads.

The discussion highlights the dual role of geomorphology and accessibility: low‑lying, flat terrain facilitates settlement and waste generation, while proximity to roads and villages determines the likelihood of illegal disposal. The spatial patterns differ across sub‑Carpathian depressions, plateau sectors, and corridor valleys, illustrating that vulnerability is not uniform even within a single county.

In the conclusions, the authors argue that GIS is an effective tool for revealing how local geographic conditions shape illegal dumping risk. The most exposed locations are the susceptible factors situated within 250 m–500 m of built‑up areas. The presented vulnerability model, validated by on‑the‑ground observations, can serve as a decision‑support instrument for Environmental Impact Assessments (EIA) and for municipal planners tasked with prioritising waste‑management interventions in rural settings. Moreover, because the methodology relies on widely available spatial data and a transparent weighting scheme, it can be adapted to other regions facing similar challenges.