Groundwater aquifers are an important source of water supply for agriculture, industry and drinking. The present study investigates the changes in the quality of groundwater using geostatistical methods in the Ravar plain during a 10-year period. In this study, after selecting the appropriate spatial interpolation method to draw water quality parameters such as TDS, SAR and EC, zoning maps of Ravar plain were provided for three periods of time: the first period (2002-2005), an intermediate period (2006-2009) and the final period (2010-2012) in two seasons using ArcGIS 10.1. For this purpose, data were evaluated in GS+ 5.1 software, after calculation, the best model with the lowest estimated error was selected for zoning water quality parameters. Because of the lowest estimation error, Kriging, Gaussian and Spherical variogram models were selected as appropriate interpolation method for zoning the quality parameters. The results of the spatial analysis of TDS showed that TDS have been increased in the study area. Due to the amount of dissolved solids, EC amount was highly variable. According to the Wilcox classification, at the end of the period, water quality of agricultural was inappropriate in most of the area which represents the increase of electrical conductivity during the period.

Understanding the key drivers of eutrophication in floodplain lakes has long been a challenge. In this study, the Chlorophyll a (Chla) variations and associated relationships with environmental stressors along the temporal hydrological connectivity gradient were investigated using a 11-year dataset in a large floodplain lake (Poyang Lake). A geostatistical method was firstly used to calculate the hydrological connectivity curves for each sampling campaign that was further classified by K-means technique. Linear mixed effect (LME) models were developed through the inclusion of the site as a random effect to identify the limiting factors of Chla variations. The results identified three clear hydrological connectivity variation patterns with remarkable connecting water area changes in Poyang Lake. Furthermore, hydrological connectivity changes exerted a great influence on environmental variables in Poyang Lake, with a decrease in nutrient concentrations as the hydrological connectivity enhanced. The Chla exhibited contrast variations with nutrient variables along the temporal hydrological connectivity gradient and generally depended on WT, DO, EC and TP, for the entire study period. Nevertheless, the relative roles of nutrient and non-nutrient variables in phytoplankton growth varied with different degrees of hydrological connectivity as confirmed by the LME models. In the low hydrological connectivity phase, the Chla dynamics were controlled only by water temperature with sufficient nutrients available. In the high hydrological connectivity phase, the synergistic influences of both nutrient and physical variables jointly limited the Chla dynamics. In addition, a significant increasing trend was observed for Chla variations from 2008 to 2018 in the HHC phase, which could largely be attributed to the elevated nutrient concentrations. This study confirmed the strong influences of hydrological connectivity on the nutrient and non-nutrient limitation of phytoplankton growth in floodplain lakes. The present study could provide new insights on the driving mechanisms underlying phytoplankton growth in floodplain lakes.

Quantifying source apportionment of potentially toxic elements (PTEs) in soils and associated human health risk (HHR) is essential for soil environment regulation and pollution risk mitigation. For this purpose, an integrated method was proposed, and applied to a dataset consisting of As, Cd, Cr, Cu, Hg, Ni, Pb, Se, and Zn in 273 soil surface samples. Positive matrix factorization (PMF) was used to quantitatively examine sources contributions of PTEs in soils; and the HHR arising from the identified source was determined by combining source profiles and health risk assessment; at last, sequential Gaussian simulation (SGS) was used to identify the areas with high HHR. Four sources were identified by PMF. Natural and agricultural sources affected all 9 PTEs contents with contributions ranging from 19.2% to 62.9%. 41.9% of Cd, 40.8% of Pb, 58.6% of Se, and 29.8% of Zn were controlled by industrial and traffic emissions. Metals smelting and mining explained 35.5%, 30.5%, and 24.9% of Cr, Cu, and Ni variations, respectively. Hg was dominated by atmospheric deposition from coal combustion and coking (58.7%). The mean values of the total non-carcinogenic risks of PTEs were 1.55 × 10⁻¹ and 9.40 × 10⁻¹ for adults and children, and the total carcinogenic risk of PTEs had an average value of 8.86 × 10⁻⁵. Based on source-oriented HHR calculation, natural and agricultural sources were the most important factor influencing HHR, explaining 51.0% and 49.1% of non-carcinogenic risks for children and adults, and 44.2% of carcinogenic risk. SGS indicated that 1.1% of the total area was identified as hazardous areas with non-carcinogens risk for children.

Mapping soil contamination enables the delineation of areas where protection measures are needed. Traditional soil sampling on a grid pattern followed by chemical analysis and geostatistical interpolation methods (GIMs), such as Kriging interpolation, can be costly, slow and not well-suited to highly heterogeneous soil environments. Here we propose a novel method to map soil contamination by combining high-resolution aerial imaging (HRAI) with machine learning algorithms. To support model establishment and validation, 1068 soil samples were collected from an arsenic (As) contaminated area in Zhongxiang, Hubei province, China. The average arsenic concentration was 39.88 mg/kg (SD = 213.70 mg/kg), with individual sample points determined as low risk (66.9%), medium risk (29.4%), or high risk (3.7%), respectively. Then, identified features were extracted from a HRAI image of the study area. Four machine learning algorithms were developed to predict As risk levels, including (i) support vector machine (SVM), (ii) multi-layer perceptron (MLP), (iii) random forest (RF), and (iii) extreme random forest (ERF). Among these, we found that the ERF algorithm performed best overall and that its prediction performance was generally better than that of traditional Kriging interpolation. The accuracy of ERF in test area 1 reached 0.87, performing better than RF (0.81), MLP (0.78) and SVM (0.77). The F1-score of ERF for discerning high-risk points in test area 1 was as high as 0.8. The complexity of the distribution of points with different risk levels was a decisive factor in model prediction ability. Identified features in the study area associated with fertilizer factories had the most important contribution to the ERF model. This study demonstrates that HRAI combined with machine learning has good potential to predict As soil risk levels.

Spatial uncertainty information of the environmental risk of soil heavy metal is crucial for precise environmental management. This study first compared three geostatistical methods for spatial simulation of soil Copper (Cu) in a peri-urban agriculture area of Wuhan city, China, that are sequential Gaussian co-simulation (CoSGS) with auxiliary in-situ portable X-ray fluorescence (PXRF) data (CoSGS_in-situ), CoSGS with auxiliary ex-situ PXRF data (CoSGS_ex-situ), and sequential Gaussian simulation without auxiliary data (SGS). Then, the environmental risk of soil Cu was assessed based on the joint thresholds of soil Cu and soil pH in the Chinese soil environmental quality standards II. The geostatistical simulated realizations of soil Cu and soil pH were used to calculate the probabilities of exceeding the joint thresholds. Validation showed that CoSGS_ex-situ is slightly better than CoSGS_in-situ in the performance of both E-type estimates (i.e., mathematical expectation estimates) and uncertainty modelling of soil Cu, and SGS is the worst. The spatial uncertainty information of both soil Cu and soil pH was transferred to the environmental risk map through the corresponding geostatistical simulated realizations. The areas with higher probabilities of exceeding the joint thresholds mainly located in the northwest and southwest of the study area. It is concluded that CoSGS_ex-situ and CoSGS_in-situ were more cost-effective than the traditional SGS in the spatial simulation of soil Cu, and the simulated realizations of soil Cu and soil pH provide a solution to the spatial assessment of the probabilities of exceeding the joint thresholds.

The contributions of major driving forces on temporal changes of heavy metals in the soil in a representative river−alluviation area at the lower of Yangtze River were successfully quantified by combining geostatistics analysis with the modified principal component scores & multiple linear regressions approach (PCS−MLR). The results showed that the temporal (2003–2014) changes of Cu, Zn, Ni and Cr presented a similar spatial distribution pattern, whereas the Cd and Hg showed the distinctive patterns. The temporal changes of soil Cu, Zn, Ni and Cr may be predominated by the emission of the shipbuilding industry, whereas the significant changes of Cd and Hg were possibly predominated by the geochemical and geographical processes, such as the erosion of the Yangtze River water and leaching because of soil acidification. The emission of metal−bearing shipbuilding industry contributed an estimated 74%–83% of the changes in concentrations of Cu, Zn, Ni and Cr, whereas the geochemical and geographical processes may contribute 58% of change of Cd in the soil and 59% of decrease of Hg.

The impact of land use type on the content of potentially toxic elements (PTEs) in the soils of the Qinghai-Tibet Plateau (QTP) and the associated ecological and human health risks has drawn great attention. Consequently, in this study, top- and subsurface soil samples were collected from areas with four different land uses (i.e., cropland, forest, grassland, and developed area) and the total contents of Cr, Cd, Cu, Pb and Zn were determined. Geostatistical analysis, self-organizing map (SOM), and positive matrix factorization (PMF), ecological risk assessment (ERA) and human health risk assessment (HRA) were applied and used to classify and identify the contamination sources and assess the potential risk. Partial least squares path modeling (PLS-PM) was applied to clarify the relationship of land use with PTE contents and risk. The PTE contents in all topsoil samples surpassed the respective background concentrations of China and corresponding subsurface concentrations. However, the ecological risk of all soil samples remained at a moderate or considerable level across the four land use types. Developed area and cropland showed a higher ecological risk than the other two land use types. Industrial discharges (32.8%), agricultural inputs (22.6%), natural sources (23.7%), and traffic emissions (20.9%) were the primary PTE sources in the tested soils, which indicate that anthropogenic activities have significantly affected soil PTE contents to a greater extent than other sources. Industrial discharge was the most prominent source of non-carcinogenic health risk, contributing 37.7% for adults and 35.2% for children of the total risk. The results of PLS-PM revealed that land use change associated with intensive human activities such as industrial activities and agricultural practices distinctly affected the PTE contents in soils of the Qinghai-Tibet Plateau.

Accurate mapping of air pollutants is essential for epidemiological studies and environmental risk assessments. Concentrations measured by air quality monitoring stations (AQMS) have primarily been used to assess the exposure of PM₂.₅. However, the low coverage and amount of monitoring stations affect the errors of spatial interpolation or geostatistical estimates. In contrast to other integrated approaches developed for improved air pollution estimates, this study utilizes data from low-cost microsensors densely deployed in Taiwan to improve the popular spatial interpolation approach called inverse distance weighting (IDW). A large dataset from thousands of low-cost sensors could improve spatial interpolation by describing the distribution of PM₂.₅ in detail. Therefore, this study presents a clustering-based method to assess the distribution of PM₂.₅. Then, a smarter IDW is performed based on correlated observations from the selected air quality stations. The publicly available data chosen for this investigation pertained to Taiwan, which has deployed 74 monitoring stations and more than 11,000 low-cost sensors since December 2020. The results of leave-one-out cross-validation indicate that there are fewer PM₂.₅ estimation errors in the developed approach than in estimations that use kriging across almost all of the months and sampled dates of 2019 and 2020, particularly those with higher PM₂.₅ spatial heterogeneities. Spatial heterogeneities could result in more significant estimation errors in mainstream approaches. The root mean square error of the monthly average estimate for PM₂.₅ ranged from 1.17 to 3.86 μg/m³. We also found that the clustering of one month characterizing the pattern of PM₂.₅ distribution could perform well in spatial interpolations based on historical data from monitoring stations. According to the information on the openaq platform, low-cost sensors are in demand in cities and areas. This trend might pave the way for the application of the proposed approach in other areas for superior exposure assessments.