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.

Living in walkable neighborhoods has been reported to be associated with a lower risk of cardiovascular disease. Features of walkable neighborhoods, however, may be related to particulate matter with an aerodynamic diameter ≤2.5 μm (PM₂.₅), which could increase risk of cardiovascular disease. The interaction effect between walkability and PM₂.₅ on risk of ischemic stroke remains to be elucidated. In this study, we recruited a total of 27,375 participants aged ≥40 years from Yinzhou District, Ningbo, Zhejiang Province, China to investigate the associations of walkability and PM₂.₅ with risk of ischemic stroke. We used amenity categories and decay functions to evaluate walkability and high-spatiotemporal-resolution land-use regression models to assess PM₂.₅ concentrations. We used Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). During a median follow-up of 4.08 years, we identified a total of 637 incident cases of ischemic stroke in the entire cohort. Higher walkability was associated with a lower risk of ischemic stroke (quartile, Q4 vs. Q1 walkability: HR = 0.59, 95% CI: 0.47–0.75), whereas PM₂.₅ was positively associated with risk of ischemic stroke (Q4 vs. Q1 PM₂.₅: HR = 1.70, 95% CI: 1.29–2.25). Furthermore, we observed a significant interaction between walkability and PM₂.₅ on risk of ischemic stroke. Walkability was inversely associated with risk of ischemic stroke at lower PM₂.₅ concentrations, but this association was attenuated with increasing PM₂.₅ concentrations. Although walkable neighborhoods appear to decrease the risk of ischemic stroke, benefits may be offset by adverse effects of PM₂.₅ exposure in the most polluted areas. These findings are meaningful for future neighborhood design, air pollution control, and stroke prevention.

Urban green spaces (UGSs) are often positively associated with the health of urban residents. However, UGSs may also have adverse health effects by releasing biogenic volatile organic compounds (BVOCs) and increasing the ambient concentrations of ozone (O₃) and secondary organic aerosols in urban areas. BVOC emissions from UGSs might be underestimated because of the lack of consideration of the UGS land-use type in urban areas. As such, in this study, we used a newly released satellite dataset, Sentinel-2, with a resolution of 10 m, to derive the classification distribution of UGSs and predict the UGS emissions of BVOCs in Beijing in 2019. The results showed that the annual emissions of BVOCs from UGSs were approximately 2.9 Gg C (95% confidence interval (CI): 2.4–3.3) in the six core districts, accounting for approximately 39% of the total UGS emissions in Beijing. Compared with the results based on Sentinel-2, the BVOC emissions might be underestimated by approximately 37% (95% CI: 11–63) using the commonly used satellite dataset. UGSs produced the highest BVOC emissions in summer (from June to August), accounting for 75.2% of the annual emissions. UGSs contributed the most to the O₃ formation potential in summer, accounting for 41.5% of the total. We could attribute a considerable amount of the O₃ concentration (27.0 μg m⁻³, 95% CI: 21.4–32.6) to the UGS BVOCs produced in the core districts of Beijing in July. The new BVOC emissions dataset based on Sentinel-2 vegetation information facilitates modeling studies on the formation of surface O₃ in urban areas and assessments of the impact of UGSs on public health.

Dismantling and recycling e-waste has been recognized as a potential emission source of rare earth elements (REEs). However, the presence of REEs in typical regional soils has yet to be studied. Given the potential health implications of such soil contamination, it is vital to study the characteristics, spatial distribution, and pollution level of REEs caused by e-waste dismantling as well as determine the influencing mechanism. This study focused on Guiyu Town as an example site, which is a typical e-waste dismantling base. From the site, 39 topsoil samples of different types were collected according to grid distribution points. Soil profiles were also collected in the dismantling and non-dismantling areas. The REE characteristic parameters showed that the REE distribution was abnormal and was affected by multiple factors. The results of the integrated pollution index showed that approximately 61.5% of soil samples were considered to be lightly polluted. Spatial distribution and correlation analysis showed that hot spots of REE-polluted soil coincided with known, main pollution sources. Moreover, there was a significant negative correlation (p ≤0.05) between the REE concentration and the distance from the pollution source. E-waste disassembly and recycling greatly affect the physical and chemical properties of the surrounding soil as well as downward migration areas. In the disassembly area, REE accumulated more easily in the surface layer (0–20 cm). Geographical detector results showed that distance factor was the main contribution factor for both light rare earth elements (LREE) and heavy rare earth element (HREE) (q = 34.59% and 53.33%, respectively). REE distribution in soil was nonlinear enhanced by different factors. Taken together, these results showed that e-waste disassembling and recycling not only directly affected the spatial distribution of REEs, but that their distribution was also affected by land use type and soil properties.

Plastic pollution represents the most widespread threaten throughout the world and, amongst aquatic habitats, freshwaters and in particular riparian zones seems to be highly disturbed. Since the plastic storage and accumulation on the riparian vegetation have not yet been deeply investigated, here, we focussed on the riparian zone's function in trapping plastic litter. To do so, we assessed the occurrence and density of plastics in different vegetated (arboreal, shrubby, herbaceous, reed, bush) and unvegetated types in 8 central Italian rivers, running in different land use contexts. Our results showed that plastic pieces, bags, bottles and food containers were the most abundant specific categories on the vegetated types, demonstrating the riparian vegetation role in trapping plastic litter. Specifically, the highest plastic density was found on the shrubby type suggesting that a tree shape retains plastics more easily than all other vegetated and unvegetated types. Shape and size classification of plastics are not significantly different between vegetated and unvegetated types. These findings allow to collect important information on how the riparian vegetation can be exploited in management activities for removing plastic litters from both freshwater and sea, being the former considered the main plastic source for the latter. This study highlights a further ecosystem service as mechanical filter provided by the riparian zone, even if further studies ought to be performed to understand the role of vegetation as plastic trap and the possible detrimental effects of plastics on the plant health status.

Microplastics are a pollutant of growing concern, capable of harming aquatic organisms and entering the food web. While freshwater microplastic research has expanded in recent years, much remains unknown regarding the sources and delivery pathways of microplastics in these environments. This review aims to address the scientific literature regarding the spatial and temporal factors affecting global freshwater microplastic distributions and abundances. A total of 75 papers, published through June 2021 and containing an earliest publication date of October 2014, was identified by a Web of Science database search. Microplastic spatial distributions are heavily influenced by anthropogenic factors, with higher concentrations reported in regions characterized by urban land cover, high population density, and wastewater treatment plant effluent. Spatial distributions may also be affected by physical watershed characteristics such as slope and elevation (positive and negative correlations with microplastic concentrations, respectively), although few studies address these factors. Temporal variables of influence include precipitation and stormwater runoff (positive correlations) and water flow/discharge (negative correlations). Despite these overarching trends, variations in study results may be due to differing scales or contributing area delineations. Thus, more rigorous and standardized spatial analytical methods are needed. Future research could simultaneously evaluate both spatial and temporal factors and incorporate finer temporal resolutions into sampling campaigns.

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM₂.₅) in high-density cities, few studies have revealed the vertical differences in PM₂.₅ exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM₂.₅ exposure that combines a high-resolution LUR model with a vertical PM₂.₅ variation model to investigate the results of horizontal and vertical mobile PM₂.₅ monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM₂.₅ variation, respectively. Vertical measurements of PM₂.₅ from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM₂.₅ changes at a certain height relative to the ground. The vertical variation model for PM₂.₅ was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM₂.₅ exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM₂.₅, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO₂) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors’ readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran’s I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO₂ concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Identifying risks to ecosystems from contaminants needs a diversity of bioindicators, to understand the effects of these contaminants on a range of taxa. Molluscs are an ideal bioindicator because they are one of the largest phyla with extremely high ecological and economic importance. The aim of this study was to evaluate if laboratory bred Potamopyrgus antipodarum has the potential to show the impact of contaminants from various land use activities and degree of pollution on a freshwater ecosystem. We assessed the impact of contaminants arising from runoff and direct discharges in Merri Creek by measuring organism level responses (survival, growth, and reproduction), and sub-organism level responses (glutathione S-transferase (GST) activity, lipid peroxidation (LPO) activity and catalase (CAT) activity) in snails after 28-d of deployment at nine sites in Merri Creek and one site in Cardinia Creek. In Merri Creek, the top two sites were reference sites (with low impact from human activities), while the rest were impact sites (impacted by various anthropogenic land uses). Cardinia Creek (an additional reference site) had lower human activity. High concentrations of heavy metals, nutrients, and/or synthetic pyrethroids (bifenthrin) dominated these sites, which are likely to have contributed towards the negative responses observed in the snails. There was little influence from environmental conditions and site location on the endpoints because we found a similar response at an additional reference site compared to the reference sites in Merri Creek. At the organism level, reproduction increased and/or reduced, while CAT was affected at the sub-organism level. Potamopyrgus antipodarum has the potential to be a sensitive bioindicator for Australian conditions because the snails responded to varying concentrations of contaminants across different land use activities and showed similar sensitivity to P. antipodarum found in other regions of the globe and other bioindicators.