We measured the concentrations of cesium isotopes (¹³³Cs, ¹³⁴Cs, and ¹³⁷Cs) in zooplankton samples collected in waters off the east coast of Japan from May 2015 to June 2020. By combining these data with those obtained previously from May 2012 to February 2015, we evaluated the long-term impacts of the Fukushima Dai-ichi Nuclear Power Plant accident on marine zooplankton. Relatively high ¹³⁷Cs concentrations in zooplankton, exceeding 10 Bq/kg-dry weight, were sporadically observed until June 2016, regardless of year or station. After May–June 2017, ¹³⁷Cs concentrations decreased to below 1 Bq/kg-dry at most stations, and by May 2020, concentrations were below 0.5 Bq/kg-dry except those off Fukushima Prefecture. Since the accident, the ¹³⁷Cs/¹³³Cs atom ratios of zooplankton samples were higher than those of ambient seawater until 2019, but in May–June 2020 the ratios matched those of seawater except off Fukushima Prefecture. Highly radioactive particles were not detected in zooplankton samples by autoradiography using imaging plates after May–June 2017, although they were before. Therefore, the persistence of elevated ¹³⁷Cs/¹³³Cs ratios in zooplankton relative to seawater for nine years after the accident was probably due to the incorporation of highly radioactive particles (cesium-bearing particles or clay-mineral aggregates with highly adsorbed radiocesium) onto/into zooplankton for several years after the accident. However, since at least May–June 2017, these elevated ratios have likely been caused by small highly radioactive particles (or larger particles disaggregated into small pieces) entering the ocean from land via rivers or directly discharged from the Fukushima Nuclear Power Plant. Microplastics enriched with radiocesium with higher ¹³⁷Cs/¹³³Cs ratios than seawater may have also contributed ¹³⁷Cs to the zooplankton.

Rivers can be sinks for potential toxic elements (PTEs) inputted in their systems by both natural and anthropic processes. Many indices have been proposed to assess the contamination degree of sediments and the environmental conditions of surficial water bodies. Above all, enrichment factor (EF) is the most used tool, but also it is the most debated for its limitations. The need for a reference element and for a background/baseline composition makes the EF method dependent on the researcher's expertise, implying that its repeatability may not be granted. Starting from the awareness that geochemical processes, bringing to compositional changes in the environmental matrices, involve multiple elements rather than individual variables, we developed a modified EF (mEF) based on the use of elemental associations. Different multivariate statistical methods (i.e. Robust Principal Component Analysis and Fuzzy Clustering), in a compositional data analysis (CoDA) perspective, were used to set all the terms of the mEF. The mEF was applied to 101 sediment samples collected from a 2 m-long core, covering a sedimentation period of about 150 years (1850–2007), located in the lower Changjiang River (China). The method resulted effective in recognizing most of the signals proceeding from the main natural and anthropogenic events which affected the lower river basin in the considered timespan. The largest geochemical variations recorded fit well the flooding events occurred; besides, the effects produced on the system by the recent socio-economic development (following the end of the civil war in 1949 and the beginning of economic reforms in 1978) and the start-up of the Three Gorges Dam (the world's largest power station since 2012) were also intercepted. The proposed method represents a step forward to enhance the effectiveness of the EF in discriminating geochemical anomalies that may be significant to assess the human historical impact on the environment.

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%.

Biomass integrated gasification combined cycle (IGCC) is attracting increased interest because it can achieve high system energy efficiency (>50%), which is predicted to increase with the increase in the solar share in biomass IGCC. This study evaluated the potential of crop residues numerically for the co-production of power and bio-fertilizer using ASPEN Plus® simulation software. The results showed that the gas yield increases with increasing temperature and decreasing pressure while the yield of bio-fertilizer is dependent on the biomass composition. The biomass with a low ash content produces high bio-fertilizer at the designated gasification temperature. The IGCC configuration conserves more energy than a directly-fired biomass power plant. In addition, the solar-assisted IGCC attains a higher net electricity output per unit of crop residue feed and achieves net thermal efficiencies of around 53%. The use of such hybrid systems offer the potential to produce 0.55 MW of electricity per unit of solar-thermal energy at a relatively low cost. The ASPEN Plus model predicted that the solar biomass-based IGCC set up is more efficient in increasing the power generation capacity than any other conversion system. The results showed that a solar to electricity efficiency of approximately 55% is achievable with potential improvements. This work will contribute for the sustainable bioenergy production as the relationship between energy production and biomass supplies very important to ensure the food security and environmental sustainability.

Human-induced temperature changes influence coastal regions, both via thermal pollution and ocean warming, which exerts profound effects on the chemistry of metals and the physiology of organisms. However, it remains unknown whether the increased temperature of discharged water or ocean warming, as a result of climate change, lead to an increase of human health risks associated with the consumption of sea foods. In this study, the influence of temperature on metal accumulation by oysters was studied in individuals collected from a coastal area affected by the thermal water discharge of the Houshi Power Plant, China. The bioaccumulation factor (BAF) and oral bioavailability (OBA) of metals in oysters was determined. Elevated temperatures led to an increase in BAF for Cu, Zn, Hg, and Cd (p < 0.05), but no change was observed for As and Pb (p > 0.05). The OBA for Cd, As, and Pb correlated positively to elevated temperatures (p < 0.05). However, for Cu and Zn, OBA was negatively correlated with increasing temperature (p < 0.05). As, Pb, and Cd in the trophically available metal (defined as a sum of heat-stable proteins, heat-denaturable proteins, and organelles) was significantly elevated at the highest temperature seawater site (site A) compared to the lowest seawater site (site B). Thus, the irregular variation of OBA for each metal may be the result of variations in the subcellular distribution of metals and the protein quality influenced by the increased temperature. Moreover, the increased temperature and increased the hazard quotient values of As and Cd (p < 0.05 for As, n = 6, p < 0.05 for Cd, n = 6), which provided an indication of the potential risks of the consumption of oysters or other seafood to future warming under climate change scenarios.

Following nine years since the Fukushima Dai-ichi Nuclear Power Plant Acciden (FDNPPA), it might be the time to draw a much clearer conclusion for the impact of FDNPPA on marine biota. In this work, the evolution of the FDNPPA derived ¹³⁴Cs, ¹³⁷Cs and ¹¹⁰ᵐAg in the neon flying squids in the Northwest Pacific from 2011 to 2018 were studied. The background level of ¹³⁷Cs in neon flying squids (<0.10 Bq/kgfᵣₑₛₕ wₑᵢgₕₜ with the average of 0.017 Bq/kgfᵣₑₛₕ wₑᵢgₕₜ) before FDNPPA were estimated. The radioactive levels of ¹³⁴Cs, ¹³⁷Cs and ¹¹⁰ᵐAg in neon flying squids decreased with time. ¹³⁴Cs and ¹¹⁰ᵐAg decreased at the half-lives of 7.6 months and 5.7 months at the population level, respectively. After May 2014, ¹³⁴Cs and ¹¹⁰ᵐAg cannot be detected and ¹³⁷Cs activities returned to the background level before FDNPPA. BCFs of cesium isotopes (3.7–17.7 with the average of 10.8) and ¹¹⁰ᵐAg (∼7 × 10⁴) for neon flying squids were estimated. The amount of ¹¹⁰ᵐAg released into the Northwest Pacific (∼20-∼26 TBq) were firstly calculated using a ¹³⁴Cs/¹¹⁰ᵐAgₐcₜᵢᵥᵢₜy ᵣₐₜᵢₒ method. Radiation dose assessment demonstrated that it was far from causing radiation harm to neon flying squids in the open ocean of Northwest Pacific and humans who ingested these neon flying squids.

This study presents the results of an integrated model developed to evaluate the environmental and health impacts of Electric Vehicle (EV) deployment in a large metropolitan area. The model combines a high-resolution chemical transport model with an emission inventory established with detailed transportation and power plant information, as well as a framework to characterize and monetize the health impacts. Our study is set in the Greater Toronto and Hamilton Area (GTHA) in Canada with bounding scenarios for 25% and 100% EV penetration rates. Our results indicate that even with the worst-case assumptions for EV electricity supply (100% natural gas), vehicle electrification can deliver substantial health benefits in the GTHA, equivalent to reductions of about 50 and 260 premature deaths per year for 25% and 100% EV penetration, compared to the base case scenario. If EVs are charged with renewable energy sources only, then electrifying all passenger vehicles can prevent 330 premature deaths per year, which is equivalent to $3.8 Billion (2016$CAD) in social benefits. When the benefit of EV deployment is normalized per vehicle, it is higher than most incentives provided by the government, indicating that EV incentives can generate high social benefits.

Mercury (Hg) is a toxic and persistent pollutant and has long-term impacts on ecological systems and human health. Coal-fired power plants (CFPPs) are the main source of anthropogenic Hg emission, and the emitted atmospheric Hg is deposited to the surrounding environments which causes soil pollution. To assess the effects of atmospheric Hg from CFPPs in China on the temperate steppe, Hg contents in the topsoil and subsoil were analyzed for samples collected from 80 sites in central Inner Mongolia during 2012–2015. The average content of Hg in topsoil and subsoil were 14.9 ± 10.4 μg kg⁻¹ and 8.9 ± 5.8 μg kg⁻¹, respectively. The principal components analysis (PCA) indicated that the soil organic matter content and atmospheric deposition were the main factors determining soil Hg content in Inner Mongolia. We used the power plant impact factor (PPIF) to evaluate the impacts of the surrounding CFPPs. The PPIF results showed the most positive correlation with Hg content in topsoil at more than 400 km distances, indicating that the contribution of the long-range transport of Hg emitted from CFPPs is regional in scale. Considering the potential of Hg accumulation in soil, long-term and regional measurements of soil Hg and stricter emission-limit standards for power plants should be implemented to control soil Hg pollution in China.