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.

The livelihoods of poor people living in rural areas of Indus Basin Irrigation System (IBIS) of Pakistan depend largely on irrigated agriculture. Water duties in IBIS are mainly calculated based on crop-specific evapotranspiration. Recent studies show that ignoring the spatial variability of factors affecting the crop water requirements can affect the crop production. The objective of the current study is thus to identify the factors which can affect the water duties in IBIS, map these factors by GIS, and then develop the irrigation response units (IRUs), an area representing the unique combinations of factors affecting the gross irrigation requirements (GIR). The Lower Chenab Canal (LCC) irrigation scheme, the largest irrigation scheme of the IBIS, is selected as a case. Groundwater quality, groundwater levels, soil salinity, soil texture, and crop types are identified as the main factors for IRUs. GIS along with gamma design software GS + was used to delineate the IRUs in the large irrigation scheme. This resulted in a total of 84 IRUs in the large irrigation scheme based on similar biophysical factors. This study provided the empathy of suitable tactics to increase water management and productivity in LCC. It will be conceivable to investigate a whole irrigation canal command in parts (considering the field-level variations) and to give definite tactics for management.

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.

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.

Particulate air pollution in cities comprises a variety of harmful compounds, including fine iron rich particles, which can persist in the air for long time, increasing the adverse exposure of humans and living things to them. We studied street tree (among other species, Cordyline australis, Fraxinus excelsior and F. pensylvanica) barks as biological collectors of these ubiquitous airborne particles in cities. Properties were determined by the environmental magnetism method, inductively coupled plasma optical emission spectrometry and scanning electron microscopy, and analyzed by geostatistical methods. Trapped particles are characterized as low-coercivity (mean ± s.d. value of remanent coercivity Hcᵣ = 37.0 ± 2.4 mT) magnetite-like minerals produced by a common pollution source identified as traffic derived emissions. Most of these Fe rich particles are inhalable (PM₂.₅), as determined by the anhysteretic ratio χARM/χ (0.1–1 μm) and scanning electron microscopy (<1 μm), and host a variety of potentially toxic elements (Cr, Mo, Ni, and V). Contents of magnetic particles vary in the study area as observed by magnetic proxies for pollution, such as mass specific magnetic susceptibility χ (18.4–218 × 10⁻⁸ m³ kg⁻¹) and in situ magnetic susceptibility κᵢₛ (0.2–20.2 × 10⁻⁵ SI). The last parameter allows us doing in situ magnetic biomonitoring, being convenient because of species preservation, measurement time, and fast data processing for producing prediction maps of magnetic particle pollution.

Nitrogen is one of the most significant pollutants in the Yangtze River estuary (YRE), China. Reliable estimation of nitrogen concentration in the water is crucial for assessment of the water quality of the estuary. Because ocean fronts exist in the YRE, which divide water masses into different regions, it is necessary to account for the heterogeneity of the water surface when predicting nitrogen concentrations. A new geostatistical method, called spatiotemporal point mean of surface with non-homogeneity (ST-PMSN), is proposed to model the non-stationary spatiotemporal random process of nitrogen concentrations between 2004 and 2013 in the YRE. The method considers the spatiotemporal correlation of surface water nitrogen and uses information from both sides of a boundary for heterogeneous water masses. Comparing with several other interpolating methods, including spatial ordinary kriging (OK), stratified ordinary kriging (SOK), point mean of surface with non-homogeneity (P-MSN), spatiotemporal ordinary kriging (STK), and stratified spatiotemporal ordinary kriging (SSTK), the cross-validation results show that ST-PMSN has the highest accuracy, followed by SSTK, STK, P-MSN, SOK, and OK in descending order. ST-PMSN is therefore demonstrated to be effective in estimating the nitrogen pollutant concentrations in a stratified estuary. According to interpolated nitrogen concentrations in the YRE, water quality has generally deteriorated—with fluctuations—from 2004 to 2013. The average annual reduction in area of water quality of Grades I and II from 2004 to 2013 was 1.10%. At the same time, the average annual increase in area of water quality of Grades III and IV was 0.89% and that of Grade V was 0.21%. The results of this study provide a new and more accurate interpolating method for assessing the pollutant concentration in the marine and offers guidance for more precise classification of water quality in the YRE.

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km², with a median of 0.4 samples per km². The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA).

Spatially explicit estimates of critical loads of nitrogen (N) deposition (CLNdₑₚ) for nutrient enrichment in aquatic ecosystems were developed for the Rocky Mountains, USA, using a geostatistical approach. The lowest CLNdₑₚ estimates (<1.5 ± 1 kg N ha⁻¹ yr⁻¹) occurred in high-elevation basins with steep slopes, sparse vegetation, and abundance of exposed bedrock and talus. These areas often correspond with areas of high N deposition (>3 kg N ha⁻¹ yr⁻¹), resulting in CLNdₑₚ exceedances ≥1.5 ± 1 kg N ha⁻¹ yr⁻¹. CLNdₑₚ and CLNdₑₚ exceedances exhibit substantial spatial variability related to basin characteristics and are highly sensitive to the NO₃ ⁻ threshold at which ecological effects are thought to occur. Based on an NO₃ ⁻ threshold of 0.5 μmol L⁻¹, N deposition exceeds CLNdₑₚ in 21 ± 8% of the study area; thus, broad areas of the Rocky Mountains may be impacted by excess N deposition, with greatest impacts at high elevations.

Elevated concentrations of arsenic were detected in surface soils adjacent to a smelting complex in northern Canada. We evaluated the cancer risks caused by exposure to arsenic in two communities through combining geostatistical simulation with demographic data and dose-response models in a framework. Distribution of arsenic was first estimated using geostatistical circulant-embedding simulation method. We then evaluated the exposures from inadvertent ingestion, inhalation and dermal contact. Risks of skin caner and three internal cancers were estimated at both grid scale and census-unit scale using parametric dose-response models. Results indicated that local residents could face non-negligible cancer risks (skin cancer and liver cancer mainly). Uncertainties of risk estimates were discussed from the aspects of arsenic concentrations, exposed population and dose-response model. Reducing uncertainties would require additional soil sampling, epidemic records as well as complementary studies on land use, demographic variation, outdoor activities and bioavailability of arsenic. Cancer risks induced by arsenic in soil were evaluated using geostatistical simulation and dose-response model.