Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China

Abstract

Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0–20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions.

Soil Contamination in the Problem Areas of Agrarian Slovakia

“…It is significantly present in the so-called geochemical anomalies that are numerous in Slovakia. Cadmium is a risk element; its mobility is mainly influenced by soil reaction, humus content as well as quality, chemical composition of the soil solution and the sorption complex [51,52,57]. In our research, abovelimit Cd content was found and measured at all investigated localities.…”

Cadmium Content in Soil and Tomato (Lycopersicon Esculentum Miller) Around Industrial Areas of Bangladesh

“…At low concentrations, Cd acts as a photophosphorylation inhibitor in spinach chloroplasts (Tukendorf 1993). Ding et al (2021) reported that Cadmium (Cd) is an instance of a heavy metal that is very much toxic for plant growth. Therefore, as the physiological process is hampered by Cd concentration, reduced fruit weight was observed from the study.…”

“…These pollutants can be injurious for the plants and be also responsible for reducing plants life (Gheorghe and Ion 2012). Cadmium (Cd) is an instance of a heavy metal that is very much toxic for plant growth (Ding et al 2021). In particular, Cd uptake in plants occurs in the roots via essential metal transporters and is partially translocated aboveground (Umer et al 2021).…”

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

“…Neural networks apply a modeling method that is based on empirical data rather than on simplified ideal assumptions. ANN is broadly applied in various areas of environmental engineering such as air quality monitoring [20], soil quality monitoring [21], and water quality prediction [22]. The use of ANN was found to be superior in performance as it was implemented in various fields including medicine [23][24][25], geotechnical engineering [26], petroleum engineering [27], construction technology [28], building energy [29], and waste management [30], as compared to other models, such as linear and SVM models.…”