Spatial variation of the levels of polycyclic aromatic hydrocarbons (PAHs) was evaluated within an urban-industrial district where the main anthropogenic pressures are a 15 MW biomass power plant (BPP) and road traffic. The use of a high-density lichen transplant network and wind quantitative relationships made it possible to perform a hierarchical analysis of contamination. Combined uni-bi and multivariate statistical analyses of the resulting databases revealed a dual pattern. In its surroundings (local scale), the BPP affected the bioaccumulation of fluoranthene, pyrene and total PAHs, although a confounding effect of traffic (mostly petrol/gasoline engines) was evident. Spatial variation of the rate of diesel vehicles showed a significant association with that of acenaphthylene, acenaphthene, fluorene, anthracene and naphthalene. The series of high-speed wind values suggests that wind promotes diffusion rather than dispersion of the monitored PAHs. At the whole study area scale, the BPP was a source of acenaphthylene and acenaphthene, while diesel vehicles were a source of acenaphthylene. PAHs contamination strongly promotes oxidative stress (a threefold increase vs pre-exposure levels) in lichen transplants, suggesting a marked polluting effect of anthropogenic sources especially at the expense of the mycobiont. The proposed monitoring approach could improve the apportionment of the different contributions of point and linear anthropogenic sources of PAHs, mitigating the reciprocal biases affecting their spatial patterns.

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.

The infrastructures of coal-fired power plants in China have changed significantly since 2010, but the magnitude and characteristics of polycyclic aromatic hydrocarbon (PAH) emissions remain to be updated. In the present study, a unit-based PAH emission inventory for coal-fired power plants between 2010 and 2017 was constructed for Anhui Province, China. Atmospheric PAH emissions from pulverized coal (PC) and circulating fluidized bed (CFB) units in 2017 were 8600 kg and 7800 kg, respectively. The emission rates and intensities for CFB units (7.2 kg ton⁻¹ and 2.1 kg MW⁻¹) were significantly higher than those for PC units (1.1 kg ton⁻¹ and 0.19 kg MW⁻¹), primarily because CFB boilers were operated at lower combustion temperatures and poor combustion conditions compared to PC boilers. The distribution patterns of PAH emissions across different age groups largely reflected the time periods for constructing coal-fired units in Anhui and for the transition of small units to large ones. The accomplishment of ultralow emission technologies and phase-out of outdated coal-fired units were responsible for the decreasing trend of PAH emissions between 2012 and 2017. The warmer summer in 2013 and 2017 and colder winter in 2011 compared to other years probably caused increased use of air conditioners, resulting in increased electricity consumption and PAH emissions. Future PAH emissions would decrease by 45–57% during 2017–2030, benefitting from power plant fleet optimization, i.e., phasing out outdated coal-fired units and replacing them with large ones. With the best available optimized power plant fleets and end-of-pipe control measures accomplished in Anhui’s CFPPs, PAH emissions in 2030 would potentially be reduced by 56–65%.

There is currently a significant gap in knowledge about the emission and deposition of mercury (Hg) from coal-fired power plants in Australia. To fill this gap, we propose a novel method that combines several sources of information (stratigraphic data, hydrodynamic modelling and atmospheric modelling), to identify the sources and fates of Hg emitted from coal-fired power plants. The stratigraphic record from Lake Macquarie (Australia) shows that mercury deposition increased up to 7-times since the 1950s, which is when coal-fired power plants were commissioned in the catchment. The stratigraphy also shows a decrease in Hg deposition with power plant retrofits. Using results from multiple models (statistical modelling, hydrodynamic modelling, particle density modelling and atmospheric emissions modelling), we found that ash dams contribute little Hg to Lake Macquarie. Instead, most of the Hg contamination in the lake is a result of atmospheric emissions from the power plants, and these power plants are also depositing Hg in the urban areas to the west of the lake. Our results demonstrate that the multi-proxy approach demonstrated in the paper can be used to provide clues as to the source of Hg, so that appropriate mitigation strategies and regulatory frameworks can be implemented.

Owing to its physicochemical similarity to strontium (Sr), calcium (Ca) was tested as a key component of a soil washing solution for Sr-contaminated soil collected near a nuclear power plant. A four-factor, three-level Box–Behnken experimental design combined with response surface modeling was employed to determine the optimal Sr washing condition for Ca-based solution. The Ca concentration (0.1–1 M), liquid-to-soil ratio (5–20), washing time (0.5–2 h), and pH (2.0–7.0) were tested as the independent variables. From the Box–Behnken design, 27 sets of experimental conditions were selected, and a second-order polynomial regression equation was derived. The significance of the independent parameters and interactions was tested by analysis of variance. Ca concentration was found to be the most influential factor. To determine whether the four variables were independent, three-dimensional (3D) response surface plots were established. The optimal washing condition was determined to be as follows: 1 M Ca, L/S ratio of 20, 1 h washing, and pH = 2. Under this condition, the highest Sr removal efficiency (68.2%) was achieved on a soil contaminated with 90.1 mg/kg of Sr. Results from five-step sequential extraction before and after washing showed that 84.0% and 82.9% of exchangeable and carbonate-bound Sr were released, respectively. In addition, more tightly bound Sr, such as Fe/Mn oxides-bound and organic matter-bound Sr, were also removed (86.2% and 64.5% removal, respectively).

Environmental exposure to pollution generated by mining and burning coal is inevitable for people living nearby. Therefore, the aim of this study was to evaluate the influence of coal dust on health conditions and genomic instability of individuals who live near coal mines and thermoelectric power plants, and to relate the results to inorganic elements and inflammatory responses. Thus, we evaluated 284 individuals from four cities in the south of Brazil around a region with coal mines and a thermoelectric power plant (one city was considered a negative control). The results of the Comet assay and Micronucleus (MN) test did not show a genotoxic or mutagenic effect related to environmental exposure to coal, but the inflammatory cytokine tumor necrosis factor-α (TNF-α) was increased in all cities around the power plant when compared to the control conditions. Higher levels of MN were associated with body mass index and cardiovascular risk, and higher levels of Damage Index (DI), TNF-α and interleukin1β (IL-1β) with number of cigarettes/day. Principal component analysis (PCA) was used to integrate DNA damage and inflammatory results with inorganic elements. This study also demonstrated the relationship between zinc and MN, copper, and interleukin10 (IL-10), and among silicon and sulfur with DI and nucleoplasmic bridge. A relationship was also observed between the reduction of inorganic elements and both aging and quality of health. The use of different methodologies and the relationship between the results obtained in these studies, including different lifestyles, can increase the understanding of the interaction between this mineral and the health status of residents of regions affected by coal pollution.

From the mid-June to mid-July 2020, there was a massive bloom of Creseise acicula nearby the waters of Daya Bay Nuclear Power Plant Base (DNPP base). In order to find out the spatiotemporal dynamic characteristics of C. acicula and the main factors related to its outbreak and extinction, acoustic surveys and in-situ observations were performed. The results showed that the average abundance of C. acicula at the in-situ observation site fluctuated with the tidal rhythm. Furthermore, a horizontal migration pattern during ebb tide and a vertical subsidence trend of C. acicula was found. The outbreak of C. acicula bloom nearby the waters of DNPP base was the result of the joint action of water temperature, salinity and food availability etc. The extinction of C. acicula was mainly related to the adhesion of Licmophora, predation pressure from phytoplanktivorous fishes (such as Sardinella lemuru and Dussumieria elopsoides) and human intervention.

Seawater chlorination is widely used for coastal, marine industries for the prevention of fouling. Using a choice chamber system, we investigated the influence of chlorinated seawater at typical concentrations occurring near chlorinated cooling water discharges, on the behaviour of juvenile seabass (Dicentrachus labrax). These studies showed that there was evidence of an effect of chlorination, with models predicting 22% fewer fish present in the chlorine dosed chamber compared to the undosed chamber in all control runs (mean number of fish in the dosed chamber in all control runs was 2.23 in comparison to 1.74 when chlorine was present). These data suggest that when food is available D. labrax will enter areas that are at mean chlorination levels of 0.04 mg l⁻¹, which are typical close to the discharge of power station cooling water but are more likely to move away once the food supply is reduced.

In this paper, a marine environmental sensitivity index system was constructed based on three factors: the marine biological species sensitivity, the marine ecological value sensitivity and the social and economic sensitivity. Combined with numerical simulations of ¹³⁷Cs in the Beibu Gulf under the condition of a level-7 nuclear accident, a comprehensive risk assessment of marine radioactivity under the simulated accident in the Beibu Gulf was carried out. The results show that the areas at high risk and medium-to-high risk to marine radioactivity are mainly concentrated within 30 km of the Fangchenggang Nuclear Power Plant. The medium-risk areas are mainly distributed in sea areas within 30–50 km of the nuclear power plant, and the other sea areas are low-risk areas. The distribution of the risk sources and the characteristics of the regional tidal currents are the main factors affecting the magnitude and distribution of the comprehensive risk of regional marine radioactivity.

Anthropogenic radionuclides are among those human impacts, which can be seen widely in the marine and terrestrial ecosystems. Fallout from the 1986 Chernobyl nuclear power plant accident has rendered the Baltic Sea as the most polluted marine body in the world with respect to ¹³⁷Cs. This research investigated sediment cores from 56 sites around the Gulf of Bothnia, Baltic Sea. Radioactivity from ¹³⁷Cs in sediments has generally declined due to natural/radioactive decay of ¹³⁷Cs over the last decades. However, ¹³⁷Cs contents in subsurface sediments remain at elevated levels compared to pre-Chernobyl levels. The highest ¹³⁷Cs activity contents in subsurface sediments (>4000 Bg kg⁻¹) occur in coastal areas including estuaries. These areas often experience severe anthropogenic pressure. The southern Bothnian Sea, Kvarken archipelago, and southern Bothnian Bay all show elevated ¹³⁷Cs values in subsurface sediments. Sedimentary ¹³⁷Cs can also help constrain recent rates of sedimentation. Post-Chernobyl sedimentation rates in the Gulf of Bothnia varied from 0.1 to 4.8 cm/year with an average sedimentation rate of 0.54 cm/year.