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.

Optimizing landscape pattern to reduce the risk of non-point source (NPS) pollution is an effective measure to improve river water quality. The “source-sink” landscape theory is a recent research tool for landscape pattern analysis that can effectively integrate landscape type, area, spatial location, and topographic features to depict the spatial heterogeneity of NPS pollution. Based on this theory, we quantitatively analyzed the influence of “source-sink” landscape pattern on the river water quality in one of the most intensive agricultural watersheds in Southeastern China. The results indicated that the proportion of “sink” landscape (68.59%) was greater than that of “source” landscape (31.41%) in the study area. In addition, when elevation and slope increased, the “source” landscape proportion decreased, and the “sink” landscape proportion increased. Nitrogen (N) and phosphorus (P) pollutants in rivers showed significant seasonal and spatial variations. Farmland was the primary source of nitrate nitrogen (NO₃⁻-N) and total nitrogen (TN) pollution, whereas residential land was the primary source of ammonium nitrogen (NH₄⁺-N) and total phosphorus (TP) pollution. Intensively cultivated areas and densely inhabited areas degraded water quality despite high proportions of forest land. The four “source-sink” landscape indices (LWLI, LWLI'e, LWLI's, LWLI'd) had significant positive correlations with NO₃⁻-N and TN and weak correlations with NH₄⁺-N and TP. The capacity of LWLI to quantify the NPS pollution was greater in agricultural areas than in residential areas. The “source-sink” landscape thresholds resulted in abrupt changes in water quality. When LWLI was ∼0.35, the probability of river water quality degradation increased sharply. The results suggest the importance of optimizing the “source-sink” landscape pattern for mitigating agricultural NPS pollution and provide policy makers with adequate new information on the agroecosystem-environmental interface in highly developed agricultural watersheds.

Southwestern China contains the largest and most well-developed karst region in the world, and the potentially toxic elements (PTEs) content in the soils of the region is remarkably high. To explore the internal and external control factors and sources of soil PTEs enrichment in this area and to provide a basis for the treatment of PTE pollution, 113 soil samples were collected from Hengxian County, a karst region in Guangxi Province, southwestern China. The importance of eighteen influencing factors including parent material, weathering, physicochemical properties, topography and human activities were quantitatively analyzed by (partial) redundancy analysis. The sources of PTEs were identified using the Pb isotope ratio and absolute principal component score/multiple linear regression (APCS-MLR) model. The contents of all soil PTEs were higher than the corresponding background values of Guangxi soils. The contents in Cu, Zn, Cd, Hg and Pb were the highest in the soil from carbonate rock. The factor group of geological background and weathering explained 26.5% for the accumulation and distribution of soil PTEs and the influence of physicochemical properties was less than 2% but increased to 25.6% through interaction with weathering. Fe (47.1%), Al (42.1%), Mn (22%), chemical index of alteration (12.8%) and clay (11.9%) were the key factors affecting the soil PTEs, while the influence of human activities was weak. Pb isotope ratio and APCS-MLR classified 62.8–74% of soil PTEs as derived from natural sources, whereas 18.23% and 18.95% were derived from industrial activities and agricultural practice/traffic emissions, respectively. The Pb isotope ratio showed that the natural sources account for up to 90% of the Pb in the soil from carbonate rock, the highest contribution among the studied soils. The results of the study can provide background information on the soil PTEs contamination in the karst areas of China and other areas worldwide.

The effects of dike construction on the geomorphology and sedimentary processes of tidal flats were investigated using high-precision topographic profiling, short cores, and unmanned aviation vehicle (UAV)-assisted photogrammetry to understand their adverse consequences on the benthic ecosystem. Tidal flats at the south of Shinsi Island near one of the two sluice gates of the Saemangeum dike, display prominent morphologic features known as shelly sand ridges or cheniers (sensu Otvos, 2000) that have migrated landward about 5 m in a year. The tidal flats were dominated by erosion from winter to spring and by deposition during the remainder of the year except for the periods of heavy precipitation when tidal drainage channels became larger and deeper by headward erosion. With overall coarser-grained surface sediments, the presence of actively migrating wave-built cheniers are in stark contrast to muddy tidal flats with a monotonous morphology before the completion of the Saemangeum dike in 2006. Southeasterly waves reflected from the dike during winter to spring when north to northwesterly winds prevail account for the wave-induced onshore sediment transport and rapid morphologic changes in the tidal flats despite their location protected from offshore waves. The diversity and biomass of major macrofauna species tend to increase during rapid erosion and decrease during rapid deposition, highlighting the anthropogenic effect of dike-induced physical disturbance on the benthic ecosystem in the otherwise sheltered tidal flats.

Microplastics have been increasingly documented in freshwater ecosystems in recent years, and growing concerns have been raised about their potential environmental health risks. To assess the current state of knowledge, with a focus on the UK, a literature review of existing freshwater microplastics studies was conducted. Sampling and analytical methodologies currently used to detect, characterise and quantify microplastics were assessed and microplastic types, sources, occurrence, transport and fate, and microplastic-biota interactions in the UK’s freshwater environments were examined. Just 32% of published microplastics studies in the UK have focused on freshwater environments. These papers cover microplastic contamination of sediments, water and biota via a range of methods, rendering comparisons difficult. However, secondary microplastics are the most common type, and there are point (e.g. effluent) and diffuse (non-point, e.g. sludge) sources. Microplastic transport over a range of spatial scales and with different residence times will be influenced by particle characteristics, external forces (e.g. flow regimes), physical site characteristics (e.g. bottom topography), the degree of biofouling, and anthropogenic activity (e.g. dam release), however, there is a lack of data on this. It is predicted that impacts on biota will mirror that of the marine environment. There are many important gaps in current knowledge; field data on the transport of microplastics from diffuse sources are less available, especially in England. We provide recommendations for future research to further our understanding of microplastics in the environment and their impacts on freshwater biota in the UK.

PM₂.₅ pollution is caused by multiple factors and determining how these factors affect PM₂.₅ pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM₂.₅ and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO₂ and NO₂ concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM₂.₅ and the different factors varied with location. SO₂ emission positively affected PM₂.₅, and the impact was the strongest in the Beijing–Tianjin–Hebei (BTH) region. The impact of NO₂ was generally smaller than that of SO₂ and could be important in coastal areas. The impact of meteorological factors on PM₂.₅ was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM₂.₅ in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM₂.₅ in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.

The rapid expansion of construction related to coastal development evokes great concern about environmental risks. Recent attention has been focused mainly on factors related to the effects of waterlogging, but there is urgent need to address the potential hazard caused by artificial topography: derived changes in the elemental composition of the sediments. To reveal possible mechanisms and to assess the environmental risks of artificial topography on transition of elemental composition in the sediment at adjoining zones, a nest-random effects-combined investigation was carried out around a semi-open seawall. The results implied great changes induced by artificial topography. Not only did artificial topography alter the sediment elemental composition at sites under the effect of artificial topography, but also caused a coupling pattern transition of elements S and Cd. The biogeochemical processes associated with S were also important, as suggested by cluster analysis. The geo-accumulation index shows that artificial topography triggered the accumulation of C, N, S, Cu, Fe, Mn, Zn, Ni, Cr, Pb, As and Cd, and increased the pollution risk of C, N, S, Cu, As and Cd. Enrichment factors reveal that artificial topography is a new type of human-activity-derived Cu contamination. The heavy metal Cu was notably promoted on both the geo-accumulation index and the enrichment factor under the influence of artificial topography. Further analysis showed that the Cu content in the sediment could be fitted using equations for Al and organic carbon, which represented clay mineral sedimentation and organic matter accumulation, respectively. Copper could be a reliable indicator of environmental degradation caused by artificial topography.

We evaluated spatial patterns of mercury (Hg) deposition through analysis of foliage and forest floor samples from 45 sites across Adirondack Park, NY. Species-specific differences in foliar Hg were evident with the lowest concentrations found in first-year conifer needles and highest concentrations found in black cherry (Prunus serotina). For foliage and forest floor samples, latitude and longitude were negatively correlated with Hg concentrations, likely because of proximity to emission sources, while elevation was positively correlated with Hg concentrations. Elemental analysis showed moderately strong, positive correlations between Hg and nitrogen concentrations. The spatial pattern of Hg deposition across the Adirondacks is similar to patterns of other contaminants that originate largely from combustion sources such as nitrogen and sulfur. The results of this study suggest foliage can be used to assess spatial patterns of Hg deposition in small regions or areas of varied topography where current Hg deposition models are too coarse to predict deposition accurately.

Lanzhou is among the most seriously air-polluted cities in China as a whole, due to its unique topography, climate, industrial structure and so on. We studied the relationship between different air pollution and respiratory hospitalizations from 2001 to 2005, the total of respiratory hospital admissions were 28,057. The data were analyzed using Poisson regression models after controlling for the long time trend for air pollutants, the “day of week” effect and confounding meteorological factors. Three air pollutants (PM10, SO2, NO2) had a lag effect, the lag was 3–5 days for PM10, 1–3 days for SO2 and 1–4 days for NO2. The relative risks were calculated for increases in the inter-quartile range of the pollutants (139 μg/m3 in PM10, 61 μg/m3 in SO2 and 31 μg/m3 in NO2). Results showed that there were significant associations between air pollutants and respiratory hospital admissions, and stronger effects were observed for females and aged ≥65 yrs in Lanzhou.

Shanxi Province is one of the regions in northern China where endemic arsenicosis occurs. In this study, stepwise logistic regression was applied to analyze the statistical relationships of a dataset of arsenic (As) concentrations in groundwaters with some environmental explanatory parameters. Finally, a 2D spatial model showing the potential As-affected areas in this province was created. We identified topography, gravity, hydrologic parameters and remote sensing information as explanatory variables with high potential to predict high As risk areas. The model identifies correctly the already known endemic areas of arsenism. We estimate that the area at risk exceeding 10μgL⁻¹ As occupies approximately 8100km² in 30 counties in the province.