In predicting palm oil mill effluent (POME) degradation efficiency, previous developed quadratic model quantitatively evaluated the effects of O2 flowrate, TiO2 loadings and initial concentration of POME in labscale photocatalytic system, which however suffered from low generalization due to the overfitting behaviour. Evidently, high RMSE (131.61) and low R₂ (−630.49) obtained indicates its insufficiency in describing POME degradation at unseen factor ranges, hence verified the fact of poor generalization. To overcome this issue, several models were developed via machine learning-assisted techniques, namely Gaussian Process Regression (GPR), Linear Regression (LR), Decision Tree (DT), Supported Vector Machine (SVM) and Regression Tree Ensemble (RTE), subsequently being assessed systematically. To achieve high generalization, all models were subjected to ‘train-all-test-all’ strategy, 5-fold and 10-fold cross validation. Specifically, GPR model was furnished with high accuracy in ‘train-all-test-all’ strategy, judging from its low RMSE (1.0394) and high R₂ (0.9962), which however menaced by the risk of overfitting. In contrast, despite relatively poorer RMSE and R₂ (1.7964 and 0.9886) obtained in 5-fold cross validation, GPR model was rendered with highest generalization, while sufficiently preserving its accuracy in development process. Besides, SVM and RTE models were also demonstrated promising R₂ (0.9372 and 0.9208), which however shadowed by their high RMSEs (4.2174 and 4.7366). Furthermore, the extraordinary generalization of GPR model was coincidentally verified in 10-fold cross validation. The lowest RMSE (2.1624) and highest R₂ (0.9835) obtained with feature number of 36 asserted its sufficiency in both generalization and accuracy prospect. Other models were all rendered with slight lower R₂ (> 0.9), plausibly due to the higher RMSE (> 4.0). According to GPR model, optimized POME degradation (52.52%) can be obtained at 70 mL/min of O₂, 70.0 g/L of TiO₂ and 250 ppm of POME concentration, with only ∼3% error as compared to the actual data.

Quantifying methane (CH₄) emissions from paddy fields is essential for evaluating the environmental risks of the paddy rice production system, and improving the accuracy of CH₄ modeling is a key issue that needs to be addressed. Based on a database containing 835 field measurements, both single national and region-specific models were established to estimate CH₄ emissions from paddy fields considering different environmental factors and management patterns using 70% of the measurements. The remaining 30% of the measurements were then used for model evaluation. The performance of the region-specific model was better than that of the single national model. The region-specific model could simulate CH₄ emissions in an unbiased manner with R² values of 0.15–0.70 and efficiency values of 11–60%. The paddy rice type, water regime, organic amendment, latitude, and soil characteristics (pH and bulk density) were identified as the main drivers in the models. By inputting the high-resolution spatial data of these drivers into the established model, the CH₄ emissions from China’s paddy fields were estimated to be 4.75 Tg in 2015, with a 95% confidence interval of 4.19–5.61 Tg. The results indicated that establishing and driving a region-specific model with high-resolution data can improve the estimation accuracy of CH₄ emissions from paddy fields.

Both air pollution and dyslipidemias contributed to large number of deaths and disability-adjusted life lost years. Long-term air pollution exposure was related to changed blood lipids and risk of dyslipidemias. This study was designed to evaluate relationships between air pollutants, blood lipids and prevalence of dyslipidemias in a Chinese rural population exposed to high-level air pollution based on baseline data of The Henan Rural Cohort study. An amount of 39,057 participants from rural areas in China were included. The 3-year average exposure of air pollutants (PM2.5, PM10, NO2) was estimated by a spatiotemporal model. Logistic and linear regression models were employed to explore relationships between air pollutants, blood lipids (TC, TG, HDL-C and LDL-C) and prevalence of dyslipidemias. The three-year concentration of PM2.5, PM10 and NO2 was 72.8 ± 2.3 μg/m3, 131.5 ± 5.7 μg/m3and 39.1 ± 3.1 μg/m3, respectively. Overall, increased air pollution exposure was related to increased TC and LDL-C, while decreased TG and HDL-C. Each 1-μg/m3 increment of PM2.5 was related to 0.10% (0.07%–0.19%) increase in TC, 0.63% (0.50%–0.77%) increase in LDL-C, 2.93% (2.70%–3.16%) decrease in TG, 0.49% (0.38%–0.60%) decrease in HDL-C; and 5.7% (95%CI: 3.7%–7.6%), 4.0% (95%CI: 2.1%–6.0%) and 3.8% (95%CI: 2.5%–5.1%) increase in odds for hypercholesterolemia, hyperbetalipoproteinemia and hypoalphalipoproteinemia, respectively. Stronger associations were found in male and older participants. Findings suggest that air pollutants were associated with changed blood lipid levels and higher risk of dyslipidemias among rural population. Male and elder people should pay more attention to personal safety protection.

Mercury (Hg) is among contaminants of public concern due to its prevalent existence, high toxicity, and bioaccumulation through food chains. Elevated Hg has been detected in seafood from the East China Sea (ECS), which is one of the largest marginal seas and an important fishing region in the northwestern Pacific Ocean. However, there is still a lack of knowledge on the distribution of Hg species and their controlling factors in the ECS water column, thus preventing the understanding of Hg cycling and the assessment of Hg risks in the ECS. In this study, two cruises were conducted in October 2014 and June 2015 in order to investigate the distribution of total Hg (THg) and methylmercury (MeHg) and their controlling factors in the ECS. The concentrations of THg and MeHg were determined to be 4.2 ± 2.8 ng/L (THg) and 0.25 ± 0.13 ng/L (MeHg) in water from the ECS. The level of Hg in the ECS occupied the higher rank among the marginal seas, thus indicating significant Hg contamination in this system. Both the THg and MeHg presented complicated spatial distribution patterns in the ECS, with high concentration areas located in both the nearshore and offshore areas. Statistical analyses suggest that temperature (T) and Hg in sediment may be the controlling factors for THg distribution, while dissolved organic matter (DOM), T, and MeHg in the sediment may be the controlling factors for MeHg distribution in the seawater of the ECS. The relative importance of these environmental factors in Hg distribution depends on the water depth. T-salinity (S) diagram analyses showed that water mass mixing may also play an important role in controlling THg and MeHg distribution in the coastal ECS.

Polybrominated diphenyl ethers (PBDEs) are a series of highly persistent organic pollutants (POPs) ubiquitously distributed in marine environments. As key primary producers, microalgae are the start of PBDEs bioaccumulations and vulnerable to their toxicities. In order to deeply investigate the toxic mechanism of PBDEs on microalgal cells, the occurrence of programmed cell death (PCD) in a model diatom Thalassiosira pseudonana and its possible mediating mechanism were studied. The results indicated: cell death of T. pseudonana happened under the stress of BDE-47, which was proved to be PCD based on the correlations with three biochemical markers (DNA fragmentation, phosphatidylserine externalization and caspase activity) and three molecular markers [Metacaspase 2 gene (TpMC2), Death-associated protein gene (DAP3) and Death-specific protein 1 gene (TpDSP1)]; Furthermore, the changes of cellular ROS levels were correlated with the PCD markers and the dead cell rates, and the cell membrane and the chloroplast were identified as the major ROS production sites. Therefore, we concluded that PCD might be an important toxic mechanism of PBDEs on microalgal cells, and that chloroplast- and cell membrane-produced ROS was an important signaling molecule to mediate the PCD activation process. Our research firstly indicated microalgal PCD could be induced by PBDEs, and increased our knowledge of the toxic mechanisms by which POPs affect microalgal cells.

Trophic magnification factor (TMF) of persistent toxic substances (PTSs: Hg, PCBs, PAHs, and styrene oligomers (SOs)) in a coastal food web (12 fish and four invertebrates) was determined in Ulsan Bay, South Korea. The nitrogen stable isotope ratios (δ¹⁵N) of amino acids [δ¹⁵NGₗᵤ₋Pₕₑ based on glutamic acid (δ¹⁵NGₗᵤ) and phenylalanine (δ¹⁵NPₕₑ)] were used to estimate the trophic position (TPGₗᵤ₋Pₕₑ) of organisms. The TPGₗᵤ₋Pₕₑ of organisms ranged from 1.64 to 3.69, which was lower than TP estimated by δ¹⁵N of bulk particulate organic matter (TPBᵤₗₖ: 2.46–4.21). Mercury and CB 138, 153, 187, and 180 were biomagnified through the whole food web (TMF > 1), while other PTSs, such as PAHs and SOs were not (biodilution of SOs firstly reported). In particular, the trophic transfer of PTSs was pronounced in the resident fish (e.g., rock bream, sea perch, Korean rockfish). Of note, CB 99, 101, 118, and 183 were additionally found to be biomagnifying PTSs in these species. Thus, fish residency appears to represent an important factor in determining the TMF of PTSs in the coastal environment. Overall, δ¹⁵NGₗᵤ₋Pₕₑ provided accurate TPs of organisms and could be applied to determine the trophic transfer of PTSs in coastal food webs.

Northern China is a significant source of dust source in Central Asia. Thus, high-resolution analysis of dust storms and comparison of dust sources in different regions of northern China are important to clarify the formation mechanism of East Asian dust storms and predict or even prevent such storms. Here, we analyzed spatiotemporal trends in dust storms that occurred in three main dust source regions during 1960–2007: Taklimakan Desert (western region [WR]), Badain Jaran and Tengger Deserts (middle region [MR]), and Otindag Sandy Land (eastern region [ER]). We analyzed daily dust storm frequency (DSF) at the 10-day scale (first [FTDM], middle [MTDM], and last [LTDM] 10 days of a month), and investigated the association of dust storm occurrences with meteorological factors. The 10-day DSF was greatest in the FTDM (accounting for 77.14% of monthly occurrences) in the WR, MTDM (45.85%) in the MR, and LTDM (72.12%) in the ER, showing a clear trend of movement from the WR to the ER. Temporal analysis of DSF revealed trend changes over time at annual and 10-day scales, with mutation points at 1985 and 2000. We applied single-factor and multiple-factor analyses to explore the driving mechanisms of DSF at the 10-day scale. Among single factors, a low wind-speed threshold, high solar radiation, and high evaporation were correlated with a high DSF, effectively explaining the variations in DSF at the 10-day scale; however, temperature, relative humidity, and precipitation poorly explained variations in DSF. Similarly, multiple-factor analysis using a classification and regression tree revealed that maximum wind speed was a major influencing factor of dust storm occurrence at the 10-day scale, followed by relative humidity, evaporation, and solar radiation; temperature and precipitation had weak influences. These findings help clarify the mechanisms of dust storm occurrence in East Asia.

Pharmaceutical pollution is now recognised as a major emerging agent of global change. Increasingly, pharmaceutical pollutants are documented to disrupt ecologically important physiological and behavioural traits in exposed wildlife. However, little is known about potential impacts of pharmaceutical exposure on among-individual variation in these traits, despite phenotypic diversity being critical for population resilience to environmental change. Furthermore, although wildlife commonly experience multiple stressors contemporaneously, potential interactive effects between pharmaceuticals and biological stressors—such as predation threat—remain poorly understood. To redress this, we investigated the impacts of long-term exposure to the pervasive pharmaceutical pollutant fluoxetine (Prozac®) on among-individual variation in metabolic and behavioural traits, and the combined impacts of fluoxetine exposure and predation threat on mean metabolic and behavioural traits in a freshwater fish, the guppy (Poecilia reticulata). Using a mesocosm system, guppy populations were exposed for 15 months to one of two field-realistic levels of fluoxetine (nominal concentrations: 30 and 300 ng/L) or a solvent control. Fish from these populations were then tested for metabolic rate (oxygen uptake) and behaviour (activity), both before and after experiencing one of three levels of a predation treatment: an empty tank, a non-predatory fish (Melanotaenia splendida) or a predatory fish (Leiopotherapon unicolor). Guppies from both fluoxetine treatments had ∼70% lower among-individual variation in their activity levels, compared to unexposed fish. Similarly, fluoxetine exposure at the higher dosage was associated with a significant (26%) reduction in individual-level variation in oxygen uptake relative to unexposed fish. In addition, mean baseline metabolic rate was disrupted in low-fluoxetine exposed fish, although mean metabolic and behavioural responses to predation threat were not affected. Overall, our study demonstrates that long-term exposure to a pervasive pharmaceutical pollutant alters ecologically relevant traits in fish and erodes among-individual variability, which may be detrimental to the stability of contaminated populations globally.

Large amounts of wastewater discharge have emerged as a burden in the process of industrialization and urbanization. In this study, a dynamic wastewater-induced input-output model is developed to systematically analyze the related situation. The developed model is applied to Guangdong Province, China to analyze its prominent characteristics from 2002 to 2015. Combining input-output analysis, ecological network analysis and structural decomposition analysis, the developed model reveals issues of direct and indirect discharges, relationships among various discharges, and driving forces of wastewater discharges. It is uncovered that Primary Manufacturing and Advanced Manufacturing dominate the system because of significant temporal and spatial variations in wastewater discharge. In addition, Manufacturing of paper, computer and machinery and Services are the key industries responsible for large amounts of wastewater discharge and unhealthy source-discharge relationships. The largest wastewater discharge occurred in 2005 and indirect wastewater discharge is the main form. Furthermore, final demand is found to be the biggest driving force of wastewater discharge. Finally, a three-phase policy implementation system implemented in stages proposes solutions to wastewater problems.

Membrane concentrated landfill leachate (MCLL) contains large amounts of recalcitrant organic matter that cause potential hazards to the environment. Knowledge on the compositional variation of MCLL during treatment is important for a better understanding on the degradation pathway of organic pollutants. In this work, the structural change of MCLL during Fenton oxidation process was examined using spectroscopic techniques. The removal rates of COD, TOC and UV254 reached 78.9 ± 1.3%, 70.2 ± 1.4% and 90.64 ± 1.6%, respectively, under the optimal condition (i.e., dosage of H2O2 = 9.0 mL/200 mL, H2O2/Fe(II) molar ratio = 3.0, pH = 3.0, time = 40 min). Spectral analyses suggested that aromatic/CC structure and CO bonds in MCLL can be successfully destroyed by Fenton oxidation, resulting in a decrease in molecular weight. One fulvic-like and one humic-like components were identified in MCLL, both of which can be removed by Fenton treatment. In addition, two-dimensional correlation spectroscopic analyses suggested the oxidative changes of MCLL structure in the order of fulvic-like component/unsaturated conjugated bond > aromatic structure > humic-like component. The results may provide a new insight to the understanding on the structure variation of MCLL during treatment, which is beneficial for the design of cost-effective treatment strategies.