Erratum to: Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

Background and Objectives: It is important to understand the temporal and spatial distributions of standing wood carbon storage in forests to maintain ecological balance and forest dynamics. Such information can provide technical and data support for promoting ecological construction, formulating different afforestation policies, and implementing forest management strategies. Long-term series of Landsat 5 (Thematic Mapper, TM) and Landsat 8 (Operational Land Imager, OLI) remote sensing images and digital elevation models (DEM), as well as multiphase survey data, provide new opportunities for research on the temporal and spatial distributions of standing wood carbon storage in forests. Methods: The extracted remote sensing factors, terrain factors, and forest stand factors were analyzed with stepwise regression in relation to standing wood carbon storage to identify significant influential factors, build a global ordinary least squares (OLS) model and a linear mixed model (LMM), and construct a local geographically weighted regression (GWR), multiscale geographically weighted regression model (MGWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Model evaluation indicators were used to calculate residual Moran’s I values, and the optimal model was selected to explore the spatiotemporal dynamics of standing wood carbon storage in the Liangshui Nature Reserve. Results: Remote sensing factors, topographic factors (Slope), and stand factors (Age and DBH) were significantly correlated with standing wood carbon storage, and the constructed global models exhibited fitting effects inferior to those of the established local models. LMM is also used as a global model to add random effects on the basis of OLS, and R2 is increased to 0.52 compared with OLS. The local models based on geographically weighted regression, namely, GWR, MGWR, TWR, and GTWR, all have good performance. Compared with OLS, the R2 is increased to 0.572, 0.589, 0.643, and 0.734, and the fitting effect of GTWR is the best. GTWR can overcome spatial autocorrelation and temporal autocorrelation problems, with a higher R2 (0.734) and a more ideal model residual than other models. This study develops a model for carbon storage (CS) considering various influential factors in the Liangshui area and provides a possible solution for the estimation of long-term carbon storage distribution.

Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium

The excess of the chemical fertilizers not only causes the environmental pollution but also has many deteriorating effects including global warming and alteration of soil microbial diversity. In conventional researches, chemical fertilizers and their concentrations are selected based on the knowledge of experts involved in the projects, which this kind of models are usually subjective. Therefore, the present study aimed to introduce the optimal concentrations of three macro elements including nitrogen (0, 100, and 200 g), potassium (0, 100, 200, and 300 g), and magnesium (0, 50, and 100 g) on fruit yield (FY), fruit length (FL), and number of rows per spike (NRPS) of greenhouse banana using analysis of variance (ANOVA) followed by post hoc LSD test and two well-known artificial neural networks (ANNs) including multilayer perceptron (MLP) and generalized regression neural network (GRNN). According to the results of ANOVA, the highest mean value of the FY was obtained with 200 g of N, 300 g of K, and 50 g of Mg. Based on the results of the present study, the both ANNs models had high predictive accuracy (R2 = 0.66-0.99) in the both training and testing data for the FY, FL, and NRPS. However, the GRNN model had better performance than MLP model for modeling and predicting the three characters of greenhouse banana. Therefore, genetic algorithm (GA) was subjected to the GRNN model in order to find the optimal amounts of N, K, and Mg for achieving the high amounts of the FY, FL, and NRPS. The GRNN-GA hybrid model confirmed that high yield of the plant could be achieved by reducing chemical fertilizers including nitrogen, potassium, and magnesium by 65, 44, and 62%, respectively, in compared to traditional method.

Soft Computing Techniques for Appraisal of Potentially Toxic Elements from Jalandhar (Punjab), India

The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a pivotal role in eliminating the weaknesses in determining PTEs in soils. This paper aims to predict the concentration of Cu, Co and Pb using neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT). Statistical performance estimation factors were rummage-sale to measure the performance of developed models. Comparison of the coefficient of correlation and root mean squared error suggest that MLP-established models perform better than BT-based models for predicting the concentration of Cu and Pb, whereas BT models perform better than MLP established models at predicting the concentration of Co.

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

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.

An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions

The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades. Several machine learning (ML) models have been developed for modeling HMs over the past two decades with outstanding progress. Although there have been a noticeable number of diverse ML models investigations, it is essential to have an informative vision on the progression of those computer aid models. In the current short review covering the simulation of heavy metals in contaminated soil, water bodies and removal from aqueous solution, numerous aspects on the methodological and conceptual HMs modeling are reviewed and discussed in detail. For instance, the limitation of the classical analytical methods, types of heavy metal dataset, necessity for new versions of ML models exploration, HM input parameters selection, ML models internal parameters tuning, performance metrics selection and the types of the modelled HM. The current review provides few outlooks in understanding the underlying od the ML models application for HM simulation. Tackling these modeling aspects is significantly essential for ML developers and environmental scientists to obtain creditability and scientific consistency in the domain of environmental science. Based on the discussed modeling aspects, it was concluded several future research directions, which will promote environmental scientists for better understanding of the underlying HMs simulation.

Characterizing Khetri copper mine environment using geospatial tools

Mines result in land use and land cover (LULC) change due to degradation of natural resources and establishment of new infrastructure for ore extraction and beneficiation. The present study was carried out to, with objectives, (1) characterize LULC change (from 1975 to 2017) in Khetri copper mine region, (2) spatial distribution of pollution indices and (3) spectral response of elemental concentration of soil and groundwater using Landstat and ASTER satellite data. The study was designed to fulfil the objectives and for the same NDVI values were calculated for LULC classification and generated maps were analyzed for landscape pattern. Spatial distribution of pollution indices calculated using geochemical data of soil and groundwater was plotted to understand the impact of contamination on landscape pattern. The correlation of spectral response of Landstat bands with heavy metals concentration was plotted to assess their possible use in quantification of heavy metals. Results show constant increase in settlements, mines and open area while vegetation cover has decreased. Landscape and class level metrics (number of patch, patch density, aggregation index and landscape shape index) indicate increase in the fragmentation of landscape in recent years. Shannon’s Evenness Index indicates increase in uniformity in landscape and it is attributed to loss of vegetation and agriculture patches. Pollution indices, Pollution Load Index for soil is high near the overburden materials and Index of Environmental Risk (IER) and Contamination Index for ground water is high near abandoned mines. Spectral bands 5 and 6 (SWIR 1) show significant negative correlation, and 9 (Cirrus) shows significant positive correlation with metal concentration in soil and water suggesting the possible use of remote sensing in assessment of metal concentration at ground level. Thus, it can be concluded that mines significantly influence the landscape pattern and remote sensing could be used for the assessment and predication of heavy metal contamination at broader scale in a cost-effective way.

Quantification of heavy metal pollution for environmental assessment of soil condition

The aim of this study was to quantify heavy metal pollution for environmental assessment of soil quality using a flexible approach based on multivariate analysis. The study was conducted using 241 soil samples collected from agricultural, urban and rangeland areas in northwestern Iran. The heavy metals causing soil pollution (SP) in the study area were determined. The efficiency of principal component analysis (PCA) and discriminate analysis (DA) were compared to identify the critical heavy metals causing SP. Fourteen soil pollution indices were developed using non-linear and linear scoring equations and different integration methods. The indices were validated using the integrated pollution and potential ecological risk indices and by comparing their ability to detect soil pollution risk levels. Chromium (Cr), lead (Pb), Zinc (Zn) and copper (Cu) were identified as the significant pollutant elements using PCA, and the main pollutant elements identified using DA comprised cadmium (Cd), Zn and Pb. DA yielded a better data set for indexing SP and indicated high pollution risks for Cd > Pb > Zn. Sources of heavy metals were reliably identified using PCA, variation assessment and interrelationship evaluation of soil variables. Cr, nickel (Ni) and cobalt (Co) were found to have geogenic sources, and anthropogenic sources controlled the accumulation of Pb, Zn, Cd and Cu in soil. Linear function and additive integration were the best scoring and integrating methods for indexing HMP. The multivariate analysis provided a reliable and rapid method for indexing and mapping soil HMP.

Spatial analysis of total dissolved solids in Dezful Aquifer: Comparison between universal and fixed rank kriging

The spatial structure and auto-correlation of total dissolved solids (TDS) in an aquifer located in southwestern part of Iran were investigated by both Moran's index and variography. Since the feature of interest was non-stationary so, conventional methods of spatial analysis were not applicable and Universal kriging (UK) as a common method for spatial prediction of features with a spatial trend along with a novel geostatistical method known as fixed rank kriging (FRK) were utilized in this respect. The results of Moran's index were consistent with that of spatial analysis by geostatistical methods indicating the dominance of spatial clusters within the extent of study area. The spatial analysis by FRK was more efficient than that of its UK counterpart however the performance of UK was reasonable enough, as well. A variable selection by random forest (RF) was applied on eleven other water quality parameters that were the main constituents of TDS to identify the main parameters influencing the observed variability of TDS. It was turned out that RF is a viable method for variable selection in the realm of environmental sciences.

Assessment of tea garden soils at An'xi County in southeast China reveals a mild threat from contamination of potentially harmful elements

An extensive study of the spatial distribution characteristics of potentially harmful elements (PHEs) in tea (Camellia sinensis (L.) O. Kuntze) garden soils and ecological risk assessment at An'xi County, the birthplace of oolong tea in China, was implemented. A total of 78 soil samples were examined to determine the concentration of five PHEs (As, Cd, Cr, Hg and Pb), soil organic matter and pH by using geostatistical approaches combined with geographical information system analysis. All PHEs presented in the study area were slightly higher than their background values for provincial and national standards except Cr. Moreover, ecological risk assessment of PHEs in the tea garden soils at An'xi County was performed by means of the Håkanson method. The average ecological potential risk index (Er) of the five PHEs followed a descending order of Cd > Hg > Pb > As > Cr, and suggested a moderate ecological risk in the study area.

Development of GP and GEP models to estimate an environmental issue induced by blasting operation

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.