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.

The role of exposure time on wetland sediment-bound phosphorus (P) biogeochemical behavior is studied in Lake Poyang after the operation of the Three Gorges Dam (TGD). The multiple P compounds primarily include orth–P (88.3%), mono–P (8.9%), DNA–P (2.1%), and pyro–P (0.8%) in the exposed sediments. A significant decreasing trend of orth–P occurred after the operation of the Three Gorges Dam (TGD), with the mean concentration decreasing from 175.9 to 142.5 mg kg⁻¹ from 2007 to 2012 (ANOVA: P < 0.05), whereas the temporal change in biogenic P showed great variability. The plant distribution pattern and the increase in plant biomass due to decreased water levels might be the reason that caused variations in the P species. Furthermore, the content of orth–P, mono–P, DNA–P, and pyro–P showed increasing trends as sediment exposure time increased. However, the enzyme hydrolysis rate of DNA–P decreased with exposure time and may cause the bioavailability of biogenic P to decrease. Despite the fact that the bioavailability of biogenic P might decline in the short term, the favorable environmental conditions for P release in sediment rewetting processes, together with the increase in orth–P and biogenic P due to extended exposure time, indicate that these large additions of P would enter the overlying water and cause water quality decline once the sediment is submerged underwater during the next wet season. An environmental process analysis showed that the increased exposure time induced sediment environmental conditions changes that played an important role in the biogeochemical cycle of P and may be an important way of P replenishment in Lake Poyang. The results of this study help provide a better understanding of the role of sediment drying/wetting cycles in nutrient biogeochemical behavior and fates in wetland ecosystems.

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM₂.₅ (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM₂.₅ estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM₂.₅ concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM₂.₅ observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM₂.₅ concentration greater than 80 μg/m³ in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM₂.₅ were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM₂.₅ estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.

Ambient fine particulate matter (PM₂.₅) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM₂.₅ spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM₂.₅. Daily average PM₂.₅ data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM₂.₅ variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM₂.₅ exposures.

In this review, global change processes have been linked to polycyclic aromatic compounds (PACs) in Canada and a first national budget of sources and sinks has been derived. Sources are dominated by wildfire emissions that affect western and northern regions of Canada disproportionately due to the location of Pacific and boreal forests and the direction of prevailing winds. Wildfire emissions are projected to increase under climate warming along with releases from the thawing of glaciers and permafrost. Residential wood combustion, domestic transportation and industry contribute the bulk of anthropogenic emissions, though they are substantially smaller than wildfire emissions and are not expected to change considerably in coming years. Other sources such as accidental spills, deforestation, and re-emission of previous industrial deposition are expected to contribute anthropogenic and biogenic PACs to nearby ecosystems. PAC sinks are less well-understood. Atmospheric deposition is similar in magnitude to anthropogenic sources. Considerable knowledge gaps preclude the estimation of environmental transformations and transboundary flows, and assessing the importance of climate change relative to shifts in population distribution and energy production is not yet possible. The outlook for PACs in the Arctic is uncertain due to conflicting assessments of competing factors and limited measurements, some of which provide a baseline but have not been followed up in recent years. Climate change has led to an increase in primary productivity in the Arctic Ocean, but PAC-related impacts on marine biota appear to be modest. The net effect of changes in ecological exposure from changing emissions and environmental conditions throughout Canada remains to be seen. Evidence suggests that the PAC budget at the national scale does not represent impacts at the local or regional level. The ability to assess future trends depends on improvements to Canada’s environmental measurement strategy and biogeochemical modelling capability.

Narragansett Bay is a temperate estuary on the Atlantic coast of Rhode Island in the north-eastern United States, which receives organic pollutants from urban and industrial activities in its watershed, though detailed knowledge on sources and fluxes is missing. Twenty-four polyethylene passive samplers were deployed in the surface water of the watershed around Narragansett Bay during June–July of 2014, to examine the spatial variability and possible sources of priority pollutants, namely dissolved polycyclic aromatic hydrocarbons (PAHs) and polybrominated diphenyl ethers (PBDEs). Dissolved ∑22PAH concentrations ranged from 3.6 to 340 ng L−1, and from 2.9 to 220 pg L−1 for ∑12PBDE. The spatial variability of the concentrations was correlated to land use pattern and population distribution, in particular with human activities within 2 km of sampling sites. River discharges derived from the concentrations of PAHs and PBDEs measured here were 10–20 times greater than their previously measured concentrations in the open waters of Narragansett Bay. These results imply that river waters are the main source of PAHs and PDBEs to the Bay and that major sink terms (e.g., sedimentation, degradation) affect their concentrations in the estuary. Predicted PAH and PBDE toxicity based on dissolved concentrations did not exceed 1 toxic unit, suggested that no toxicity occurred at the sampling sites.

A new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO2 concentrations. The model was built using NO2 concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO2 concentrations, like levels of activity intensity and NOx emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R2 = 0.53). Output predictions of annual average concentrations of NO2 were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development.

This study characterizes the distribution of two closely related, causative species of paralytic shellfish poisoning – Alexandrium fundyense and A. pacificum – within the Yellow Sea (YS) and Bohai Sea (BS). These two Alexandrium species are distinguished for the first time in a regional field study using species-specific, quantitative PCR (qPCR) based assays. Both qPCR assays target the large subunit ribosomal DNA gene and were used to analyze net-concentrated phytoplankton samples collected in May 2012. A. fundyense was mainly distributed in YS, while A. pacificum was confined to an area adjacent to the Changjiang River estuary. The different distribution of the two species is interpreted as evidence of their distinct bloom ecology. Expanded efforts implementing these assays offer the ability to discriminate the dynamics of A. fundyense and A. pacificum blooms and provide a more sound basis for monitoring toxic Alexandrium species in this region.

This study assessed the impact of secondary hard substrate, as being introduced into marine ecosystems by the establishment of wind farm pillars, on the occurrence and distribution of the moon jelly Aurelia aurita in the southwestern Baltic Sea. A two-year data sampling was conducted with removable settlement plates to assess the distribution and population development of the scyphozoan polyps. The data collected from these samples were used to set up a model with Lagrangian particle technique. The results confirm that anthropogenic created hard substrate (e.g. offshore wind farms) has the potential to increase the abundance of the A. aurita population. The distribution of wind farm borne jellyfish along Danish, German and Polish coasts indicates conflicts with further sectors, mainly energy and tourism.