In recent years, with the expansion of the Weining county in the northeast of Caohai wetland, the construction of a new port in the north, and the large-scale development of cultivated land in the east, land use patterns in lakeshore areas have changed. These changes have affected the state of lake shores water bodies in complex ways, resulting in varying degrees of local water pollution. To explore the distribution and transformation characteristics of water chemistry and heavy metals in different areas of a water body under the influence of different land uses, especially the interactions between water chemical factors and heavy metals in different areas of a water body, this study used Circos diagrams, originally used in biological genetic analysis, to visualize these interactions. This is the first time that the Circos diagram has been applied to the analysis of environmental interactions. The results showed that there are significant differences in the distribution of water chemical factors and heavy metals in different areas of the Caohai wetland. In particular, Cd is affected by anthropogenic sources. The Cd content is higher in the NCL and UL areas, which are at greater risk from pollution. The factors controlling heavy metal levels in water bodies were different in the different regions. The NCL region was mainly affected by construction excavation ore, UL was mainly affected by man-made industrial inputs, CL was mainly affected by pesticide and fertilizer inputs, and ML and FL were mainly affected by Eh and DO. The PCA results showed that the sources of heavy metals in different types of water bodies in the lakeshore zone were both natural and anthropogenic. Therefore, controlling pollutants, reducing environmental pollution inputs to the lakeshore zone, and strengthening supervision and management near wetlands may be of great significance for handling heavy metal pollution.

Microplastic pollution in estuarine and coastal environments has recently been characterised in several countries but few researchers have addressed the influence of different forms of coastal zone use on the distribution of microplastic. Here, microplastic particles were sampled in Hangzhou Bay, which is heavily influenced by a range of human activities, and their abundance, size, and polymer type characterised. The abundance of microplastics was 0.14 ± 0.12 items/m³ in water, 84.3 ± 56.6 items/kg dry weight of sediment, and between 0.25 ± 0.14 and 1.4 ± 0.37 items/individual in biota. These results show that Hangzhou Bay has a low level of microplastic contamination compared to other coastal systems in China, although abundance was spatially variable within the bay; relatively higher microplastic abundances were found in the southern area of the bay, which has adjacent industrial and urban land-use zones, while lower abundances were observed in the central and northern bay areas where mariculture, fisheries, and mineral and energy industries are most common. The relatively low microplastic abundance observed in the biota samples is consistent with the generally low values for the seawater and sediment samples. Pellets were the most common of four particle-shape classes (fibres, fragments, films, and pellets) in surface seawater, while fibres were most abundant in sediment and biota. Smaller-sized microplastics (<1.0 mm) were dominant in all samples. Microplastics in the surface seawater were dominated by low-density polypropylene and polyethylene particles, while rayon was dominant in the sediment and biota samples. Our results demonstrate that regional variability in anthropogenic activity and land-use are important controls on the spatial pattern of microplastic pollution in Hangzhou Bay.

Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R2: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.

Environmentally persistent free radicals (EPFRs) are receiving increasing concern due to their toxicity and ubiquity in the environment. To avoid restrictions imposed when using a high-volume active sampler, this study uses tree leaves to act as passive samplers to investigate the spatial distribution characteristics and sources of airborne EPFRs. Tree leaf samples were collected from 120 sites in five areas around China (each approximately 4 km × 4 km). EPFR concentrations in particles (<2 μm) on the surface of 110 leaf samples were detected, ranging from 7.5 × 10¹⁶ to 4.5 × 10¹⁹ spins/g. For the 10 N.D. samples, they were all collected from areas inaccessible by vehicles. The g-values of EPFRs on 68% leaf samples were larger than 2.004, suggesting the electron localized on the oxygen atom, and they were consistent with the road dust sample (g-value: 2.0042). Significant positive correlation was found between concentrations of elemental carbon (tracer of vehicle emissions) and EPFRs. Spatial distribution mapping showed that EPFR levels in various land uses differed noticeably. Although previous work has linked atmospheric EPFRs to waste incineration, the evidence in this study suggests that vehicle emissions, especially from heavy-duty vehicles, are the main sources. While waste incinerators with low emissions or effective dust-control devices might not be an important EPFR contributor. According to our estimation, over 90% of the EPFRs deposited on tree leaves might be attributed to automotive exhaust emissions, as a synergistic effect of primary exhausts and degradation of aromatic compounds in road dust. With adding the trapping agent into the particle samples (<2 μm), signals of hydroxyl radicals were observed. This indicates that EPFRs collected from this phytosampling method can lead to the release of reactive oxygen species (ROS) once they are inhaled by human beings. Thus, this study helps highlight EPFR “hotspots” for potential health risk identification.

Accurate estimation of historical PM2.5 exposures for epidemiological studies is challenging when extensive monitoring data are limited in duration. Here, we develop a national-scale PM2.5 exposure model for Japan using measurements recorded between 2014 and 2016 to estimate monthly means for 1987 through 2016. Our objective is to obtain accurate PM2.5 estimates for years prior to implementation of extensive PM2.5 monitoring, using observations from a limited period. We utilize a neural network to convey the non-linear relationship between the target pollutant and predictors, while incorporating the associated air pollutants. We obtain high R² values of 0.76 and 0.73 through spatial and temporal cross validation, respectively. We evaluate estimation accuracy using an independent data set and achieve an R² of 0.75. Moreover, monthly variations for 2000–2013 are well reproduced with correlation coefficients of greater than 0.78, obtained through a comparison with observations. We estimate monthly means at 1 × 1 km resolution from 1987 through 2016. The estimates show decreases in the area and population weighted means beginning in the 1990s. We successfully estimate monthly mean PM2.5 concentrations over three decades with outstanding predictive accuracy. Our findings illustrate that the presented approach achieves accurate long-term historical estimations using observations limited in duration.

Organisms and ecosystems are generally exposed to mixtures of chemicals rather than to individual chemicals, but there have been relatively few detailed analyses of the mixtures of pesticides that occur in surface waters. This study examined over 2600 water samples, analysed for between 21 and 47 pesticides, from 15 waterways that discharge to the lagoon of the Great Barrier Reef in Queensland, Australia between July 1, 2011 and June 30, 2015. Essentially all the samples (99.8%) contained detectable concentrations (>limit of detection) of pesticides and pesticide mixtures. Approximately, 10% of the samples contained no quantifiable (>limit of reporting) pesticides, 10% contained one quantifiable pesticide and 80% contained quantifiable mixtures of 2–20 pesticides. Approximately 82% of samples that contained quantifiable mixtures had more than two modes of action (MoAs), but only approximately 6% had five or more MoAs. The mode, average and median number of quantifiable pesticides in all the samples were 2, 5.1 and 4, respectively. The most commonly detected compounds both individually and in mixtures were the pesticides atrazine, diuron, imidacloprid, hexazinone, 2,4-D, and the degradation product desethylatrazine. The number of pesticides and modes of action of pesticides in mixtures differed spatially and were affected by land use. Waterways draining catchments where sugar cane was a major land use had mixtures with the most pesticides.

Intensive anthropogenic activity has triggered serious heavy metal contamination of soil. Land use and land cover (LULC) changes bear significant impacts, either directly or indirectly, on the distribution of heavy metal in soils. A total of 180 samples were acquired from various land covers at different depths, namely surface soils (020 cm) and subsurface soils (20–40 cm). Spatial interpolation, geographically weighted regression (GWR) and self-organizing map (SOM) were used to discern how variations in the spatial distributions of soil heavy metals were caused by human activities for different land uses, and how these pollutants contributed to environmental risks. The medium concentrations of Cd, Cr, Cu, Pb and Zn in surface soil all exceeded the corresponding local background values in flat cropland and developed area soil. The overall ecological risk level of the study varied from low to medium. The GWR model indicated that the land use intensity had a certain influence on the accumulation of heavy metals in the surface soil. K-means clustering of the SOM revealed that the type of LULC also contributed to the redistribution of heavy metals in the surface soil.

Veterinary antibiotics have been detected as contaminants of emerging concern in soil environment worldwide. Animal manure is frequently applied to agricultural fields to improve soil fertility, which can result in introducing large amount of antibiotics into soil environment. However, few attempts have been made to identify the spatial and temporal dynamics of veterinary antibiotics in soil at the hillslope scale with different land uses. This study was performed to explore the pattern and variability of veterinary antibiotics in the soil in response to rainfall events. Results showed that higher concentrations of veterinary antibiotics were generally found in cropland (292.6 ± 280.1 ng/g) and orchard (228.1 ± 230.5 ng/g) than in forestland (13.5 ± 9.9 ng/g). After rainfall events, antibiotics accumulated in the soil at the positions where manure was applied, especially under high-intensity rainfall conditions. However, the antibiotic concentration in soil slightly increased from the top to the bottom of hills, thus indicating the restricted contribution of runoff to antibiotic transport, especially under low-intensity rainfall conditions. In addition, most antibiotics were sequestered in the surface soil (0–10 cm), and higher antibiotic concentrations were observed in deep soil (20–40 cm) in cropland than orchard. The soil aggregate, organic matter, and clay content played important roles in antibiotic sequestration along the hillslope subject to low-, medium-, and large-amount rainfall events, respectively. This study identified that land use, rainfall conditions, and soil structures jointly affect the spatial and temporal variability of antibiotics in soils on hillslopes.

Plastic, and especially microplastic, contamination of soils has become a novel research field. After the detection of microplastics in soils, spatial distribution and dynamics are still unknown. However, the potential risks associated with plastic particles in soils cannot be sufficiently assessed without knowledge about the spatial distribution of these anthropogenic materials. Based on a spatial research approach, including soil surveys, this study quantified the mesoplastic (MEP, > 5.0 mm) and coarse microplastics (CMP, 2.0–5.0 mm) content of twelve floodplain soils. At four transects in the catchment area of the Lahn river (Germany), soils down to a depth of 2 m were examined for plastic content for the first time. MEP and CMP were detected through visual examination after sample preprocessing and ATR-FTIR analyses. Average MEP and CMP concentrations range between 2.06 kg⁻¹ (±1.55 kg⁻¹) and 1.88 kg⁻¹ (±1.49 kg⁻¹) with maximal values of 5.37 MEP kg⁻¹ to 8.59 CMP kg⁻¹. Plastic particles are heterogeneously distributed in samples. Both plastic size classes occur more frequently in topsoils than in soil layers deeper than 30 cm. The maximal depth of CMP occurrence lies between 75 and 100 cm. Most common CMP polymer type was PE-LD, followed by PP and PA. MEP and CMP particles occur frequently at near channel sides and more often on riparian strips or grassland than on farmland. Vertical distribution of CMP indicates anthropogenic relocation in topsoils and additional deep displacement through natural processes like preferential flow paths or bioturbation. By comparing sedimentation rates of the river with the maximum age of plastic particles, sedimentation as a deposition process of plastic in floodplains becomes probable. From our findings, it can be concluded that an overall widespread but spatial heterogenous contamination occurs in floodplain soils. Additionally, a complex plastic source pattern seems to appear in floodplain areas.

Evidence for effects of PM₂.₅ on systemic oxidative stress in pregnant women is limited, especially in early pregnancy. To estimate the associations between ambient PM₂.₅ exposures and biomarkers of lipid peroxidation and total antioxidant capacity (T-AOC) in women with normal early pregnancy (NEP) and women with clinically recognized early pregnancy loss (CREPL), 206 early pregnant women who had measurements of serum malondialdehyde (MDA) and T-AOC were recruited from a larger case-control study in Tianjin, China from December 2017 to July 2018. Ambient PM₂.₅ concentrations of eight single-day lags exposure time windows before blood collection at the women’s residential addresses were estimated using temporally-adjusted land use regression models. Effects of PM₂.₅ exposures on percentage change in the biomarkers were estimated using multivariable linear regression models adjusted for month, temperature, relative humidity, gestational age and other covariates. Unconstrained distributed lag models were used to estimate net cumulative effects. Increased serum MDA and T-AOC were significantly associated with increases in PM₂.₅ at several lag exposure time windows in both groups. The net effects of each interquartile range increase in PM₂.₅ over the preceding 8 days on MDA were significantly higher (p < 0.001) in CREPL [52% (95% CI: 41%, 62%)] than NEP [22% (95% CI: 9%, 36%)] women. Net effects of each interquartile range increase in PM₂.₅ over the preceding 5 days on T-AOC were significantly lower (p = 0.010) in CREPL [14% (95% CI: 9%, 19%)] than NEP [24% (95% CI: 18%, 29%)] women. Exposure to ambient PM₂.₅ may induce systemic lipid peroxidation and antioxidant response in early pregnant women. More severe lipid peroxidation and insufficient antioxidant capacity associated with PM₂.₅ was found in CREPL women than NEP women. Future studies should focus on mechanisms of individual susceptibility and interventions to reduce PM₂.₅-related oxidative stress in the first trimester.