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.

Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8-OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value (HSV), Brovey, principal component analysis (PCA), Gram-Schmidt (GS), wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least squares regression (PLSR) model developed on genetic algorithm (GA)-selected laboratory visible-near infrared-shortwave infrared (VNIR–SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements’ prediction models.

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.

Different prediction models have important effects on the accuracy of spatial distribution simulations of heavy metals in soil. This study proposes a model (RFOK) combining a random forest (RF) with ordinary kriging (OK), multi-source environmental data such as terrain elements, site environmental elements, and remote sensing data were incorporated to predict the spatial distribution of heavy arsenic (As) in soil of a certain large arsenic slag site. The predictions results of RFOK were compared with those obtained using the RF, OK, inverse distance weighted (IDW), and stepwise regression (STEPREG) models for assessment of prediction accuracy. The results showed that arsenic pollution was widely distributed and the center of the site, including arsenic slag stacking area and production area were seriously polluted. The overall spatial distribution of arsenic pollution simulated by the five models was similar, but the IDW, RF, OK, and STEPREG showed less spatial variation of soil pollution, while RFOK simulation can better express the characteristics of details in change. The cross-validation results showed that RFOK had the lowest root-mean-square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) relative to the other four models, followed by RF, OK, IDW, and STEPREG. The RMSE, MAE and MRE of RFOK decreased by 62.2%, 64.3% and 68.7%, respectively, relative to the RF model with the second highest accuracy. Compared with the traditional spatial distribution prediction model, the RFOK model proposed in this study has excellent spatial distribution prediction ability for soil heavy metal pollution with large spatial variation characteristics, which can fully explain the nonlinear relationship between pollutant content and its environmental impact elements.

Maternal exposure to air pollution is associated with poor reproductive outcomes in in vitro fertilization (IVF). However, the susceptible time windows are still not been known clearly. In the present study, we linked the air pollution data with the information of 9001 women receiving 10,467 transfer cycles from August 2014 to August 2019 in The Second Hospital of Hebei Medical University, Shijiazhuang City, China. Maternal exposure was presented as individual average daily concentrations of PM₂.₅, PM₁₀, NO₂, SO₂, CO, and O₃, which were predicted by spatiotemporal kriging model based on residential addresses. Exposure windows were divided to five periods according to the process of follicular and embryonic development in IVF. Generalized estimating equation model was used to evaluate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for association between clinical pregnancy and interquartile range increased average daily concentrations of pollutants during each exposure period. The increased PM₂.₅ (adjusted OR = 0.95, 95% CI: 0.90, 0.99), PM₁₀ (adjusted OR = 0.93, 95% CI: 0.89, 0.98), NO₂ (adjusted OR = 0.89, 95% CI: 0.85, 0.94), SO₂ (OR = 0.94, 95% CI: 0.90, 0.98), CO (adjusted OR = 0.93, 95% CI: 0.89, 0.97) whereas decreased O₃ (OR = 1.08, 95% CI: 1.02, 1.14) during the duration from preantral follicles to antral follicles were the strongest association with decreased probability of clinical pregnancy among the five periods. Especially, women aged 20–29 years old were more susceptible in preantral-antral follicle transition stage. Women aged 36–47 years old were more vulnerable during post-oocyte retrieve period. Our results suggested air pollution exposure during preantral-antral follicle transition stage was a note-worthy challenge to conceive among females receiving IVF.

Fine particulate matter (PM₂.₅) pollution has become a worldwide environmental concern because of its adverse impacts on human health. This study aimed to explore the spatiotemporal variations and influencing factors of PM₂.₅ concentrations in Beijing during the 2013–2018 period, and further analyzed the impacts of environmental protection policies implemented in recent years. Notably, this study employed various statistical methods, i.e., ordinary Kriging interpolation, spatial autocorrelation analysis, time-series analysis and the Bonferroni test, to evaluate the regional and seasonal differences of PM₂.₅ concentrations based on long-term monitoring data. The results illustrated that PM₂.₅ concentrations decreased on a yearly basis, demonstrating that air pollution control policies have achieved initial success. Furthermore, PM₂.₅ concentrations were higher in the winter and in the southern regions. Diurnal variation presented a bimodal distribution, which varied slightly with the season. Relative humidity and wind speed were the principal meteorological factors affecting the distribution of PM₂.₅ concentrations, while precipitation had essentially no effect. A high positive correlation between PM₂.₅ and gaseous pollutants (SO₂, NO₂, and CO) indirectly reflected the contribution of automobile exhaust and coal-fired emissions. Generally, PM₂.₅ concentrations demonstrated strong spatiotemporal variations, and meteorological factors and pollutant emissions played an important role in this.

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.

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.

The adverse health effects of high concentrations of ground-level ozone are well-known, but estimating exposure is difficult due to the sparseness of urban monitoring networks. This sparseness discourages the reservation of a portion of the monitoring stations for validation of interpolation techniques precisely when the risk of overfitting is greatest. In this study, we test a variety of simple spatial interpolation techniques for 8-h ozone with thousands of randomly selected subsets of data from two urban areas with monitoring stations sufficiently numerous to allow for true validation. Results indicate that ordinary kriging with only the range parameter calibrated in an exponential variogram is the generally superior method, and yields reliable confidence intervals. Sparse data sets may contain sufficient information for calibration of the range parameter even if the Moran I p-value is close to unity. R script is made available to apply the methodology to other sparsely monitored constituents.

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.