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.

Air pollution and extreme heat have been responsible for more than a million deaths in China every year, especially in densely urbanized regions. While previous studies intensively evaluated air pollution episodes and extreme heat events, a limited number of studies comprehensively assessed atmospheric hot-and-polluted-episodes (HPE) – an episode with simultaneously high levels of air pollution and temperature – which have potential adverse synergic impacts on human health. This study focused on the Pearl River Delta (PRD) region of China due to its high temperature in summer and poor air quality throughout a year. We employed geostatistical downscaling to model meteorology at a spatial resolution of 1 km, and applied a machine learning algorithm (XGBoost) to estimate a high-resolution (1 km) daily concentration of particulate matter with an aerodynamic diameter ≤2.5 μm (PM₂.₅) and ozone (O₃) for June to October over 20 years (2000–2019). Our results indicate an increasing trend (∼50%) in the frequency of HPE occurrence in the first decade (2000–2010). Conversely, the annual frequency of HPE occurrence reduced (16.7%), but its intensity increased during the second decade (2010–2019). The northern cities in the PRD region had higher levels of PM₂.₅ and O₃ than their southern counterparts. During HPEs, regional daily PM₂.₅ exceeded the World Health Organization (WHO) and Chinese guideline levels by 75% and 25%, respectively, while the O₃ exceeded the WHO O₃ standard by up to 69%. Overall, 567,063 (95% confidence interval (CI): 510,357–623,770) and 52,231 (95%CI: 26,116–78,346) excessive deaths were respectively attributable to exposure to PM₂.₅ and O₃ in the PRD region. Our findings imply the necessity and urgency to formulate co-benefit policies to mitigate the region's air pollution and heat problems.

The Yellow River Delta (YRD) wetland, the most important estuary wetland in eastern China, has an important ecosystem service function. Rapid and intensive development has inevitably led to the accumulation of potentially toxic elements (PTEs) in soils. Therefore, identifying quantitative sources and spatial distributions of PTEs is essential for soil environmental protection in the YRD. A total of 240 topsoil samples (0–20 cm) were collected in the Yellow River Delta Nature Reserve (YRDNR) and analyzed the PTE contents. To avoid the biases of the single receptor model, positive matrix factorization, factor analysis with nonnegative constraints, and maximum likelihood principal component analysis-multivariate curve resolution-alternating least squares were used for source apportionment of soil PTEs. To promote the efficiency of multivariate geostatistical simulation, a minimum/maximum autocorrelation factor-sequential Gaussian simulation was built to map the spatial patterns of PTEs. Three factors were derived by the three receptor models, and their contributions to the source explanation were similar. As, Cr, Cu, Mn, Ni, and Zn originated from natural sources, with contributions of 85.6%–96.4 %. A total of 61.5 % of Hg was associated with atmospheric deposition of coal combustion and wastewater from upstream. Agricultural activities and oil exploitation contributed 33.5 % and 15.9 % of the Cd and Pb concentrations. Spatial distributions of soil PTEs were controlled by sedimentary grain size. A total of 47.2 % of the total study area was identified as hazardous area for Cd, 10.3 % for As, and 5.4 % for Hg. This work is expected to provide references for soil pollution assessment and management of YRDNR.

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.

Potential toxic metal(loid)s (PTMs) in road dust are a major concern in relation to urban environmental quality. Identifying pollution hotspots and sources of PTMs is an essential prerequisite for pollution control and management. Herein, the concentrations, pollution and potential health risks of 8 PTMs (As, Cd, Co, Cu, Hg, Mo, Pb and Zn) in road dust from the highly urbanized areas of Nanjing were studied. Spatial occurrences and sources of PTMs were explored using geostatistics, principal component analysis (PCA) and local Moran’s index. The contamination factor (CF) results showed that Co was mainly natural in origin, while the other PTMs were polluted, with average CFs ranging from 1.4 to 11.0 as follows: Hg > Mo > Cd > Cu > Pb > Zn > As, indicating moderate to very high contamination. Except for Co and Hg, the other PTMs were heavily loaded on PC1, which explained 44.72% of the total variance. Combining the statistical results and distributions of potential sources, we deduced that industrial emissions dominated the spatial patterns of all polluted PTMs in road dust, which showed high levels in the northern parts of the study region and generally decreasing levels southwards. Moreover, Pb and Zn in the south-central area and Cd in the north-central area displayed hotspots, with maximum CFs of 5.5 (Pb), 4.2 (Zn) and 16.2 (Cd), which were related to additional automotive and railway braking emissions, respectively. The resuspension of legacy pesticides in soil is likely responsible for the As pollution hotspot in the southwestern part. Despite the high anthropogenic contributions (27% for As and 68–88% for the other metals) to the PTMs in road dust, their noncarcinogenic and carcinogenic health risks were rarely found for children and adults based on the values of the hazard index and carcinogenic risk index. However, attention still should be paid to the pollution hotspots in the northern region.

High concentration of fluoride (up to 20.9 mg/L) in groundwater with significant variation (p = 5.9E-128) among samples was reported from Birbhum district, an acknowledged fluoride endemic region in India. The groundwater samples (N = 368) were grouped based on their hydrochemical properties and aquifer geology for hydro-geochemical characterization. Friedman’s test showed p < 0.0001 confidence level which indicates that fluoride concentration among geological groups and water groups are independent. Bland-Altman plot was used to study the inter-relationships among the groups through bias value (∂) and limit of agreement (LoA). Among the geological groups, laterites and granite-gneiss groups exhibited statistically significantly difference in fluoride geochemistry; whereas the younger and older alluvium groups displayed similar characteristics. The fluoride concentration was found to be in the order Lateritic > Granite-gneiss > Older alluvium ≥ Younger alluvium. Dissolution of minerals (such as fluorite, biotite) in laterite sheeted basalt, and granite-gneiss is the main source of groundwater fluoride in the region. Fluoride concentration is also influenced by depth of water table. Hydrochemical study indicated that fluoride concentration was higher in Na–HCO₃ than in Ca–SO₄ and Ca–HCO₃ type of groundwater. The fluoride concentration were positively correlated with Na⁺ and pH and negatively correlated with the Ca²⁺ and Mg²⁺ signifying linkage with halite dissolution and calcite, dolomite precipitation. Geostatistical mapping of WQI through empirical bayesian kriging (EBK) with respect to regional optimal guideline value (0.73 mg/L) classified that groundwater in some parts of the district are unfit for drinking purpose. Health survey (N = 1767) based on Dean’s criteria for dental fluorosis indicated presence of slight to moderate dental hazard. Besides, providing baseline data for management of groundwater quality in the study area, the study demonstrated the applicability of Bland-Altman analysis and empirical bayesian kriging (EBK) in delineation and interpolation of fluoride contaminated region.

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.

Current and historic atmospheric nitrogen (N) deposition has impacted aquatic ecosystems in the Greater Yellowstone Area (GYA). Understanding the spatial variation in total atmospheric deposition (wet + dry) of N is needed to estimate air pollution deposition critical loads for sensitive aquatic ecosystems. This is particularly important for areas that have an increasing contribution of ammonia dry deposition to total N (TN), such as the GYA. High resolution geostatistical models and maps of TN deposition (wet + dry) were developed using a variety of techniques including ordinary kriging in a geographic information system, to evaluate spatial variability and identify areas of elevated loading of pollutants for the GYA. TN deposition estimates in the GYA range from <1.4 to 7.5 kg N ha−1 yr−1 and show greater variability than wet inorganic N deposition. Critical loads of TN deposition (CLTNdep) for nutrient enrichment in aquatic ecosystems range from less than 1.5 ± 1.0 kg N ha−1 yr−1 to over 4.0 ± 1.0 kg N ha−1 yr−1 and variability is controlled by differences in basin characteristics. The lowest CLTNdep estimates occurred in high elevation basins within GYA Wilderness boundaries. TN deposition maps were used to identify critical load exceedances for aquatic ecosystems. Estimated CLTNdep exceedances for the GYA range from 17% to 48% depending on the surface water nitrate (NO3−) threshold. Based on a NO3− threshold of 1.0 μmol L−1, TN deposition exceeds CLTNdep in approximately 30% of the GYA. These predictive models and maps can be used to help identify and protect sensitive ecosystems that may be impacted by excess atmospheric N deposition.

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).