The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Legacy halogenated organic pollutants, including organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs), remain ubiquitous in the environment and continue to pose potential (eco-)toxicological threats because of their ongoing releases from land-based sources. This study investigated the spatial trends of freely dissolved PCBs and OCPs by polyethylene passive samplers, and provided evidence of their land-use-based sources and ecological risk in an urbanized estuary area of Narragansett Bay. Dissolved Σ₂₉PCB concentrations ranged from 0.01 to 1.37 ng L⁻¹, and exhibited higher concentrations in the upper, more urban/built-up watershed, and in north coastal areas. Major inputs of PCBs were urban stormwater or treated wastewater that might carry past releases of Aroclors, pigment manufacturing byproducts, and volatilization-associated PCBs from ageing buildings from the Narragansett watershed to the bay. The dioxin toxicity equivalent values of Σ₅PCBs were 8.6E-03 pg L⁻¹ in water. Dissolved OCP concentrations had similar spatial trends to PCBs and were dominated by DDTs (average 230 pg L⁻¹), followed by chlordanes (average 230 pg L⁻¹), and HCB (average 22 pg L⁻¹). Secondary sources of past usage and historic contamination were expected to re-enter the surface water via atmospheric transport and deposition. The risk quotients of DDE, DDD, DDT and α-Endosulfane showed medium to high ecological risks in the northern area, while chlordane, HCB, oxychlordane, and heptachlor epoxide showed low to negligible risks in all zones. This study presented new insights into the presence, sources and transport of legacy halogenated organic contaminants in an urban estuary's watershed by combining passive samplers and geographic information system (GIS) technology. The approach is promising and could be extended to get better understand of terrestrial pollutant mobilization into estuaries affected by anthropogenic activities.

Although many researchers have identified the potential psychological benefits offered by greenness, the association between green space structures and mental disorders is not well understood. The purpose of this study was to identify associations between green space structures and the incidence of bipolar disorder. To this end, we investigated 1,907,776 individuals collected from Taiwan’s National Health Insurance Research Database. After a follow-up investigation from 2005 to 2016, among those with no history of bipolar disorder, 20,548 individuals were further found to be diagnosed with bipolar disorder. A geographic information system and landscape index were used to quantify three indices of green space structures: mean patch area (area and edge), mean fractal dimension index (shape), and mean proximity index (proximity). Additionally, greenness indices, the normalized difference vegetation index, and the enhanced vegetation index were used to confirm the association between greenness and incidence of bipolar disorder. These five indices were used to represent the individual’s exposure according to the township of the hospital that they most frequently visited with symptoms of the common cold. Spearman’s correlation analysis was performed to select variables by considering their collinearity. Subsequently, the frailty model for each index was used to examine the specific associations between those respective indices and the incidence of bipolar disorder by adjusting for related risk factors, such as socioeconomic status, metabolic syndrome, and air pollution. A negative association was identified between the mean patch area and the mean proximity index, and the incidence of bipolar disorder. In contrast, a positive association was found between the mean fractal dimension index and the incidence of bipolar disorder. We observed similar results in sensitivity testing and subgroup analysis. Exposure to green spaces with a larger area, greater proximity, lower complexity, and greener area may reduce the risk of bipolar disorder.

With the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of measurements. In order to prevent air pollution, pollutant parameters must be evaluated within the framework of a model. Recently, in order to obtain objective and more sensitive results with regard to air pollution nowadays, studies, which use machine learning algorithms in artificial intelligence technologies, have been carried out. In this study, PM₁₀ concentrations, which are obtained from 7 stations in Ankara province in Turkey, were trained with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN). The PM₁₀ concentrations of the years 2009–2017 of 6 stations in Ankara were given as input, and the PM₁₀ concentrations of the seventh station for the year 2018 were predicted. The model development stage was repeated for each station, and the performance and error rates of the algorithms were determined by comparing the results produced by the algorithms with the actual results. The best results were provided with ANN (R² = 0.58, RMSE = 20.8, MAE = 14.4). The spatial distribution of the estimated concentration results was provided through Geographic Information System (GIS), and spatial strategies for improving air pollution over land use were established.

Malaysia is a tropical country that is highly dependent on surface water for its raw water supply. Unfortunately, surface water is vulnerable to pollution, especially in developed and dense urban catchments. Therefore, in this study, a methodology was developed for an extensive temporal water quality index (WQI) and classification analysis, simulations of various pollutant discharge scenarios using QUAL2K software, and maps with NH₃–N as the core pollutant using an integrated QUAL2K-GIS. It was found that most of the water quality stations are categorized as Class III (slightly polluted to polluted). These stations are surrounded by residential areas, industries, workshops, restaurants and wet markets that contribute to the poor water quality levels. Additionally, low WQI values were reported in 2010 owing to development and agricultural activities. However, the WQI values improved during the wet season. High concentrations of NH₃–N were found in the basin, especially during dry weather conditions. Three scenarios were simulated, i.e. 10%, 50% and 70% of pollution discharge into Skudai river using a calibrated and validated QUAL2K model. Model performance was evaluated using the relative percentage difference. An inclusive graph showing the current conditions and pollution reduction scenarios with respect to the distance of Skudai river and its tributaries is developed to determine the WQI classification. Comprehensive water quality maps based on NH₃–N as the core pollutant are developed using integrated QUAL2K-GIS to illustrate the overall condition of the Skudai river. High NH₃–N in the Skudai River affects water treatment plant operations. Pollution control of more than 90% is required to improve the water quality classification to Class II. The methodology and analysis developed in this study can assist various stakeholders and authorities in identifying problematic areas and determining the required percentage of pollution reduction to improve the Skudai River water quality.

Urbanization is conducive to promoting social development and improving living standards. However, the changing land use attributed to urbanization has placed both the environment and humans at risk. Based on the long-term monitoring and the land use change during 2010–2017, we investigated the exposure of fluoride (F) and arsenic (As) in groundwater. We analyzed the temporal and spatial variation of F and As from different land use changes. The study assessed health risk for children by calculating carcinogenic risk and non-carcinogenic risk. Furthermore, we mapped the distribution pattern of F and As using GIS. For the 768 water samples collected from 2010 to 2017, F concentrations ranged between 0.10 and 5.70 mg L⁻¹ (M = 0.68 ± 0.02 mg L⁻¹), As concentrations ranged between 0.50 and 71.50 μg L⁻¹ (M = 4.28 ± 0.28 μg L⁻¹). A concerning result showed that 6.77% of F concentrations larger than 1.5 mg L⁻¹ and 11.46% of As concentrations larger than 10 μg L⁻¹ based on the recommendation by WHO, respectively. Results proved that land use change caused by human activity increased groundwater pollution and placed human health at risk. High F and As risk were found in southern Taiyuan City. In particular, the groundwater of industrial land suffered from more severe pollution, especially at the frontier of urban and suburban areas in the southern part of Taiyuan City. Land use change attributed to industrial land resulted in major increases in the F and As concentrations in groundwater across 2010–2017. Both carcinogenic risk and non-carcinogenic risk in 2016–2017 were higher than that in 2010–2015. Rational land use planning, strict groundwater protection policies and the regular monitoring of pollution levels are necessary in order to prevent the adverse health of residents.

Useful information on the potential radon risk in existing buildings can be obtained by combining data from sources such as potential risk maps, the ‘Sistema de Información sobre Ocupación del Suelo de España’ (SIOSE) [information system on land occupancy in Spain], cadastral data on built property and population surveys. The present study proposes a method for identifying urban land, premises and individuals potentially subject to radon risk. The procedure draws from geographic information systems (GIS) pooled at the municipal scale and data on buildings possibly affected. The method quantifies the magnitude of the problem in the form of indicators on the buildings, number of premises and gross floor area that may be affected in each risk category. The findings are classified by type of use: residential, educational or office. That information may guide health/prevention policies by targeting areas to be measured based on risk category, or protection policies geared to the construction industry by estimating the number of buildings in need of treatment or remediation. Application of the methodology to Greater Madrid showed that 47% of the municipalities have houses located in high radon risk areas. Using cadastral data to zoom in on those at highest risk yielded information on the floor area of the vulnerable (basement, ground and first storey) premises, which could then be compared to the total. In small towns, the area affected differed only scantly from the total, given the substantial proportion of low-rise buildings in such municipalities.

Pesticides move to surface water via various pathways including surface runoff, spray drift and subsurface flow. Little is known about the relative contributions of surface runoff and spray drift in agricultural watersheds. This study develops a modeling framework to address the contribution of spray drift to the total loadings of pesticides in receiving water bodies. The modeling framework consists of a GIS module for identifying drift potential, the AgDRIFT model for simulating spray drift, and the Soil and Water Assessment Tool (SWAT) for simulating various hydrological and landscape processes including surface runoff and transport of pesticides. The modeling framework was applied on the Orestimba Creek Watershed, California. Monitoring data collected from daily samples were used for model evaluation. Pesticide mass deposition on the Orestimba Creek ranged from 0.08 to 6.09% of applied mass. Monitoring data suggests that surface runoff was the major pathway for pesticide entering water bodies, accounting for 76% of the annual loading; the rest 24% from spray drift. The results from the modeling framework showed 81 and 19%, respectively, for runoff and spray drift. Spray drift contributed over half of the mass loading during summer months. The slightly lower spray drift contribution as predicted by the modeling framework was mainly due to SWAT's under-prediction of pesticide mass loading during summer and over-prediction of the loading during winter. Although model simulations were associated with various sources of uncertainties, the overall performance of the modeling framework was satisfactory as evaluated by multiple statistics: for simulation of daily flow, the Nash-Sutcliffe Efficiency Coefficient (NSE) ranged from 0.61 to 0.74 and the percent bias (PBIAS) < 28%; for daily pesticide loading, NSE = 0.18 and PBIAS = −1.6%. This modeling framework will be useful for assessing the relative exposure from pesticides related to spray drift and runoff in receiving waters and the design of management practices for mitigating pesticide exposure within a watershed.

Multi-elemental isotopic approach associated with a land-use characteristic sampling strategy may be relevant for conducting biomonitoring studies to determine the spatial extent of atmospheric contamination sources. In this work, we investigated how the combined isotopic signatures in epiphytic lichens of two major metallic pollutants, lead (²⁰⁶Pb/²⁰⁷Pb) and mercury (δ²⁰²Hg, Δ¹⁹⁹Hg), together with the isotopic composition of nitrogen and carbon (δ¹⁵N, δ¹³C), can be used to better constrain atmospheric contamination inputs. To this end, an intensive and integrated sampling strategy based on land-use characteristics (Geographic information system, GIS) over a meso-scale area (Pyrénées-Atlantiques, SW France) was applied to more than 90 sampling stations. To depict potential relationships between such multi-elemental isotopic fingerprint and land-use characteristics, multivariate analysis was carried out. Combined Pb and Hg isotopic signatures resolved spatially the contribution of background atmospheric inputs from long range transport, from local legacy contamination (i.e. Pb) or actual industrial inputs (i.e. Pb and Hg from steel industry). Application of clustering multivariate analysis to all studied isotopes provided a new assessment of the region in accordance with the land-use characteristics and anthropogenic pressures.

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).