The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less than 10 μm in diameter (PM10μm). To do this, we use four geostatistical interpolation methods: Ordinary Kriging, Universal Kriging, Simple Kriging, and Ordinary Cokriging with Gaussian semivariogram, to estimate the spatial distribution surface for three mentioned air pollutants in Tehran’s atmosphere. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during 2012 and 2013 for 00UTC. Finally, we evaluate the Kriging estimated surfaces using three statistical validation indexes: mean absolute error (MAE), root mean square error (RMSE) that can be divided into systematic and unsystematic errors (RMSES, RMSEU), and D-Willmot. Estimated standard errors surface or uncertainty band of each estimated pollutant surface was also developed. The results indicated that using two auxiliary variables that have significant correlation with CO, the ordinary Cokriginga scheme for CO consistently outperforms all interpolation methods for estimating this pollutant and simple Kriging is the best model for estimation of NO2 and PM10. According to optimal model, the highest concentrations of PM10 are observed in the marginal areas of Tehran while the highest concentrations of NO2 and CO are observed in the central and northern district of Tehran.

Exposure of the general population to airborne metals remains poorly estimated despite the potential health risks. Passive moss biomonitoring can proxy air quality at fine resolution over large areas, mainly in rural areas. We adapted the technique to urban areas to develop fine concentration maps for several metals for Constances cohort's participants. We sampled Grimmia pulvinata in 77 and 51 cemeteries within ∼50 km of Paris and Lyon city centers, respectively. We developed land-use regression models for 14 metals including cadmium, lead, and antimony; potential predictors included the amount of urban, agricultural, forest, and water around cemeteries, population density, altitude, and distance to major roads. We used both kriging with external drift and land use regression followed by residual kriging when necessary to derive concentration maps (500 × 500 m) for each metal and region. Both approaches led to similar results. The most frequent predictors were the amount of urban, agricultural, or forest areas. Depending on the metal, the models explained part of the spatial variability, from 6% for vanadium in Lyon to 84% for antimony in Paris, but mostly between 20% and 60%, with better results for metals emitted by human activities. Moss biomonitoring in cemeteries proves efficient for obtaining airborne metal exposures in urban areas for the most common metals.

Nitrogen dioxide (NO₂) is an important air pollutant that causes direct harms to the environment and human health. Ground NO₂ mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO₂ concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R² values of 0.84 and 0.79. The annual mean and standard deviation of ground NO₂ concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m³, with that in 0.6% of China’s area (10% of the population) exceeding the annual air quality standard (40 μg/m³). The ground NO₂ concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO₂ was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO₂ concentrations across all of China. This was also an early application to use the satellite-estimated ground NO₂ data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO₂ data with high spatiotemporal resolution have value in advancing environmental and health research in China.

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.

The traditional incremental lifetime cancer risk (ILCR) model of urban soil polycyclic aromatic hydrocarbon (PAH) health risk assessment has a large spatial scale and commonly calculates relevant statistics by regarding the whole area as a geographic unit but fails to consider the high heterogeneity of the PAH distribution and differences in population susceptibility and density in an area. Therefore, the risk assessment spatial performance is insufficient and does not reflect the characteristics of cities, which are centered on human activities and serve the needs of humans, thus making it difficult to effectively support PAH prevention and treatment measures in cities. Here, the random forest model combined with the kriging residual model (RFerr-K) is used to estimate high-precision PAH distributions, separately considering the exposure characteristics of children and adults with different susceptibilities, and kindergarten point-of-interest (POI) and population density index (PDI) data were used to estimate the distributions of the kindergarten children and adults in the study area. Through the refined expression of these three dimensions, a new spatially explicit model of the incremental lifetime cancer-causing population distribution (MapPILCR) was constructed, and the risk threshold range delineation method was proposed to accurately identify regional risk levels. The results showed that the RFerr-K model significantly improves the accuracy of PAH prediction. The susceptibility index (SI) of children is 45% higher than that of adults, and POI and PDI data can be used effectively in population distribution estimation. The MapPILCR model provides a useful method for the spatially explicit assessment of the cancer risk of urban populations to inspire urban pollution grid management.

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.

The concentrations of Cd, Cr and Pb in soil samples and As, Cd, Cr and Pb in plant specimens were analyzed in an arid area in central Iran. Plants were categorized into desert-adapted (Haloxylon ammodendron, Atraphaxis spinosa and Artemisia persica) and non-desert species. It was found that the trace element (TE) accumulating potential of the desert species (Haloxylon ammodendron and Artemisia persica) with a mean value of 0.1 mg kg⁻¹ for Cd was significantly higher than that of the majority of the non-desert species with an average of 0.05 mg kg⁻¹. Artemisia also had a high As accumulating capability with a mean level of 0.8 mg kg⁻¹ in comparison with an average of 0.2 mg kg⁻¹ for most of the other plant species. The mean values of Cr and Pb in Haloxylon ammodendron and Artemisia persica were 5 and 3 mg kg⁻¹, respectively. Among the desert-adapted plants, Atraphaxis proved to be a species with high Cr and Pb accumulating potential, as well. The geoaccumulation index and the overall pollution scores indicated that the highest environmental risk was related to Cd. Different statistical analyses were used to study the spatial patterns of soil Cd and their connections with pollution sources. The variogram was estimated using a classical approach (weighted least squares) and was compared with that of the posterior summaries that resulted from the Bayesian technique, which lay within the 95% Bayesian credible quantile intervals (BIC) of posterior parameter distributions. The prediction of cadmium values at un-sampled locations was implemented by multi-Gaussian kriging and sequential Gaussian simulation methods. The prediction maps showed that the region most contaminated by Cd was the north-eastern part of the study area, which was linked to mining activities, while agricultural influence contributed less in this respect.

Recent work identified the need for site-specific Pb bioaccessibility evaluation and scaled contaminant modeling. Pb heterogeneity has made bioaccessibility characterization difficult, and complicated distribution models. Using field testing, bioaccessibility measurement, Integrated Exposure Uptake and Biokinetic (IEUBK) modeling, and geospatial techniques, we propose a framework for conducting applied risk-based, multiscale assessment. This framework was tested and implemented in Burlington, VT, an area of old housing stock and high Pb burden (up to 15 000 mg kg−1) derived primarily from paint. After analyzing local soil samples for total and bioaccessible Pb, it was determined that bioaccessible and total Pb were well correlated in this area, through which an average bioaccessibility parameter was derived approximating Pb bioaccessibility for this soil type and Pb impact. This parameter was used with the IEUBK to recommend the local limit for residential soil Pb be reduced from 400 to 360 mg kg−1, taking into consideration the lowering of the blood lead level threshold for Pb poisoning from 10 to 5 μg dL−1 by the Centers for Disease Control (CDC). Geospatial investigation incorporated samples collected during this investigation and samples from a high school summer science academy, and relied on three techniques, used at different scales: kriging of total and background Pb alone, kriging of total and background Pb with housing age as a well-sampled, well-correlated secondary variable (cokriging), and inverse distance weighting of total and bioaccessible Pb. Modeling at different scales allowed for characterization of Pb impact at single sites as well as citywide. Model maps show positive correlation between areas of older housing and areas of high Pb burden, as well as potential at different scales for reducing the effects of Pb heterogeneity.

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).