● Increased DAAO offsets 3/4 of the decrease of DAAP in 2013–2020.

PM_2.5 concentration declined significantly nationwide, while O₃ concentration increased in most regions in China in 2013–2020. Recent evidences proved that peak season O₃ is related to increased death risk from non-accidental and respiratory diseases. Based on these new evidences, we estimate excess deaths associated with long-term exposure to ambient PM_2.5 and O₃ in China following the counterfactual analytic framework from Global Burden Disease. Excess deaths from non-accidental diseases associated with long-term exposure to ambient O₃ in China reaches to 579 (95% confidential interval (CI): 93, 990) thousand in 2020, which has been significantly underestimated in previous studies. In addition, the increased excess deaths associated with long-term O₃ exposure (234 (95% CI: 177, 282) thousand) in 2013–2020 offset three quarters of the avoided excess deaths (302 (95% CI: 244, 366) thousand) mainly due to PM_2.5 exposure reduction. In key regions (the North China Plain, the Yangtze River Delta and the Fen-Wei Plain), the former is even larger than the latter, particularly in 2017–2020. Health benefit of PM_2.5 concentration reduction offsets the adverse effects of population growth and aging on excess deaths attributed to PM_2.5 exposure. Increase of excess deaths associated with O₃ exposure is mainly due to the strong increase of O₃ concentration, followed by population aging. Considering the faster population aging process in the future, collaborative control, and faster reduction of PM_2.5 and O₃ are needed to reduce the associated excess deaths.

● A review of machine learning (ML) for spatial prediction of soil contamination.

Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.

● Data quality assessment criteria for MP/NPs in food products were developed.

Data on the occurrence of microplastics and nanoplastics (MP/NPs) in foods have been used to assess the human health risk caused by the consumption of MP/NPs. The reliability of the data, however, remains unclear because of the lack of international standards for the analysis of MP/NPs in foods. Therefore, the data quality needs to be assessed for accurate health risk assessment. This study developed 10 criteria applicable to the quality assessment of data on MP/NPs in foods. Accordingly, the reliability of 71 data records (69 of them only focused on MPs) was assessed by assigning a score of 2 (reliable without restrictions), 1 (reliable but with restrictions), or 0 (unreliable) on each criterion. The results showed that only three data records scored 2 or 1 on all criteria, and six data records scored 0 on as many as six criteria. A total of 58 data records did not include information on positive controls, and 12 data records did not conduct the polymer identification, which could result in the overestimation or underestimation of MP/NPs. Our results also indicated that the data quality of unprocessed foods was more reliable than that of processed foods. Furthermore, we proposed a quality assurance and quality control protocol to investigate MP/NPs in foods. Notably, the characteristics of MP/NPs used in toxicological studies and those existing in foods showed a remarkable discrepancy, causing the uncertainty of health risk assessment. Therefore, both the estimated exposure of MP/NPs and the claimed potential health risks should be treated with caution.

● Haze formation in China is highly correlated with iron and steel industry.

Recent years have witnessed significant improvement in China’s air quality. Strict environmental protection measures have led to significant decreases in sulfur dioxide (SO₂), nitrogen oxides (NO_x), and particulate matter (PM) emissions since 2013. But there is no denying that the air quality in 135 cities is inferior to reaching the Ambient Air Quality Standards (GB 3095–2012) in 2020. In terms of temporal, geographic, and historical aspects, we have analyzed the potential connections between China’s air quality and the iron and steel industry. The non-target volatile organic compounds (VOCs) emissions from iron and steel industry, especially from the iron ore sinter process, may be an underappreciated index imposing a negative effect on the surrounding areas of China. Therefore, we appeal the authorities to pay more attention on VOCs emission from the iron and steel industry and establish new environmental standards. And different iron steel flue gas pollutants will be eliminated concurrently with the promotion and application of new technology.

● Microwave-assisted catalytic NH₃-SCR reaction over spinel oxides is carried out.

Microwave-assisted selective catalytic reduction of nitrogen oxides (NO_x) was investigated over Ni-based metal oxides. The NiMn₂O₄ and NiCo₂O₄ catalysts were synthesized by the co-precipitation method and their activities were evaluated as potential candidate catalysts for low-temperature NH₃-SCR in a microwave field. The physicochemical properties and structures of the catalysts were characterized by X-ray diffraction (XRD), Scanning electron microscope (SEM), N₂-physisorption, NO adsorption-desorption in the microwave field, H₂-temperature programmed reduction (H₂-TPR) and NH₃-temperature programmed desorption (NH₃-TPD). The results verified that microwave radiation reduced the reaction temperature required for NH₃-SCR compared to conventional heating, which needed less energy. For the NiMn₂O₄ catalyst, the catalytic efficiency exceeded 90% at 70 °C and reached 96.8% at 110 °C in the microwave field. Meanwhile, the NiMn₂O₄ also exhibited excellent low-temperature NH₃-SCR reaction performance under conventional heating conditions, which is due to the high BET specific surface area, more suitable redox property, good NO adsorption-desorption in the microwave field and rich acidic sites.

● Titanium-based flow-through electrode achieved high Cr(VI) reduction efficiency.

Flow-through electrodes have been demonstrated to be effective for electroreduction of Cr(VI), but shortcomings are tedious preparation and short lifetimes. Herein, porous titanium available in the market was studied as a flow-through electrode for Cr(VI) electroreduction. In addition, the intelligent prediction of electrolytic performance based on a back propagation neural network (BPNN) was developed. Voltametric studies revealed that Cr(VI) electroreduction was a diffusion-controlled process. Use of the flow-through mode achieved a high limiting diffusion current as a result of enhanced mass transfer and favorable kinetics. Electroreduction of Cr(VI) in the flow-through system was 1.95 times higher than in a parallel-plate electrode system. When the influent (initial pH 2.0 and 106 mg/L Cr(VI)) was treated at 5.0 V and a flux of 51 L/(h·m²), a reduction efficiency of ~99.9% was obtained without cyclic electrolysis process. Sulfate served as the supporting electrolyte and pH regulator, as reactive CrSO₇²⁻ species were formed as a result of feeding HSO₄⁻. Cr(III) was confirmed as the final product due to the sequential three-electron transport or disproportionation of the intermediate. The developed BPNN model achieved good prediction accuracy with respect to Cr(VI) electroreduction with a high correlation coefficient (R² = 0.943). Additionally, the electroreduction efficiencies for various operating inputs were predicted based on the BPNN model, which demonstrates the evolutionary role of intelligent systems in future electrochemical technologies.

● Data acquisition and pre-processing for wastewater treatment were summarized.

The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.

● High amounts of microplastics are released to receiving media from WWTPs.

Plastic particles smaller than 5 mm are microplastics. They are among the significant pollutants that recently attracted attention. Great quantities of microplastics enter the sewage system daily and reach wastewater treatment plants (WWTPs). As a result, WWTPs are potential microplastic sources. Hence, they create a pathway for microplastics to reach aquatic environments with treated wastewater discharge. Studies on microplastic characterization in WWTPs have gained momentum in academia. This study investigates the abundance, size, shape, color, polymer type, and removal efficiencies of microplastics in a municipal wastewater treatment plant (WWTP) in Denizli/Turkey. The results showed that the dominant microplastic shape in wastewater samples was fibers (41.78%–60.77%) in the 100–500 µm (58.57%–80.07%) size range. Most of the microplastics were transparent-white (32.86%–58.93%). The dominant polymer types were polyethylene (54.05%) and polyethylene vinyl acetate (37.84%) in raw wastewater. Furthermore, the microplastic removal efficiencies of the Denizli Central WWTP as a whole and for individual treatment units were evaluated. Although the microplastic pollution removal efficiency of the Denizli Central WWTP was over 95%, the microplastic concentration discharged daily into the receiving environment was considerably high (1.28 × 10¹⁰ MP/d). Thus, Denizli Central WWTP effluents result in a high volume of emissions in terms of microplastic pollution with a significant daily discharge to the Çürüksu Stream.

● Methods for estimating the aging of environmental micro-plastics were highlighted.

With the increasing production of petroleum-based plastics, the problem of environmental pollution caused by plastics has aroused widespread concern. Microplastics, which are formed by the fragmentation of macro plastics, are bio-accumulate easily due to their small size and slow degradation under natural conditions. The aging of plastics is an inevitable process for their degradation and enhancement of adsorption performance toward pollutants due to a series of changes in their physiochemical properties, which significantly increase the toxicity and harm of plastics. Therefore, studies should focus on the aging process of microplastics through reasonable characterization methods to promote the aging process and prevent white pollution. This review summarizes the latest progress in natural aging process and characterization methods to determine the natural aging mechanism of microplastics. In addition, recent advances in the artificial aging of microplastic pollutants are reviewed. The degradation status and by-products of biodegradable plastics in the natural environment and whether they can truly solve the plastic pollution problem have been discussed. Findings from the literature pointed out that the aging process of microplastics lacks professional and exclusive characterization methods, which include qualitative and quantitative analyses. To lessen the toxicity of microplastics in the environment, future research directions have been suggested based on existing problems in the current research. This review could provide a systematic reference for in-depth exploration of the aging mechanism and behavior of microplastics in natural and artificial systems.

● TiO₂/ZSM-11 was prepared by a facile solid state dispersion method.

Photocatalytic degradation is a promising way to eliminate dye contaminants. In this work, a series of TiO₂/ZSM-11 (TZ) nanocomposites were prepared using a facile solid state dispersion method. Methyl orange (MO), methylene blue (MB), and rhodamine B (RhB) were intentionally chosen as target substrates in the photocatalytic degradation reactions. Compared to pristine TiO₂, negative effect was observed on MO degradation while promoted kinetics were collected on MB and RhB over TZ composites. Moreover, a much higher photocatalytic rate was interestingly achieved on RhB than MB, which indicated that a new factor has to be included other than the widely accepted electrostatic interaction mechanism to fully understand the selective photodegradation reactions. Systematic characterizations showed that TiO₂ and ZSM-11 physically mixed and maintained both the whole framework and local structure without chemical interaction. The different trends observed in surface area and the photo-absorption ability of TZ composites with reaction performance further excluded both as the promotion mechanism. Instead, adsorption energies predicted by molecular dynamics simulations suggested that differences in the adsorption strength played a critical role. This work provided a deep mechanistic understanding of the selective photocatalytic degradation of dyes reactions, which helps to rationally design highly efficient photocatalysts.

● Column experiments with an inclined slope were applied to simulate NH₄–N transport.

Ion-absorbed rare earth mines, leached in situ, retain a large amount of ammonium nitrogen (NH₄–N) that continuously releases into the surrounding environments. However, quantitative descriptions and predictions of the transport of NH₄–N across mining area with hill slopes are not fully established. Here, laboratory column experiments were designed with an inclined slope (a sand box) to examine the spatial temporal transport of NH₄–N in soils collected from the ionic rare earth elements (REE) mining area. An HYDRUS-2D model simulation of the experimental data over time showed that soils had a strong adsorption capacity toward NH₄–N. Chemical non-equilibrium model (CNEM) could well simulate the transport of NH₄–N through the soil-packed columns. The simulation of the transport-adsorption processes at three flow rates of leaching agents revealed that low flow rate enabled a longer residence time and an increased NH₄-N adsorption, but reduced the extraction efficiency for REE. During the subsequent rainwater washing process, the presence of slope resulted in the leaching of NH₄–N on the surface of the slope, while the leaching of NH₄–N deep inside the column was inhibited. Furthermore, the high-intensity rainfall significantly increased the leaching, highlighting the importance of considering the impact of extreme weather conditions during the leaching process. Overall, our study advances the understanding of the transport of NH₄–N in mining area with hills, the impact of flow rates of leaching agents and precipitation intensities, and presents as a feasible modeling method to evaluate the environmental risks of NH₄–N pollution during and post REE in situ mining activities.

● The availability of PD-anammox was investigated with higher NO₃^––N concentration.

Partial denitrification-anammox (PD-anammox) is an innovative process to remove nitrate (NO₃^––N) and ammonia (NH₄⁺–N) simultaneously from wastewater. Stable operation of the PD-anammox process relies on the synergy and competition between anammox bacteria and denitrifiers. However, the mechanism of metabolic between the functional bacteria in the PD-anammox system remains unclear, especially in the treatment of high-strength wastewater. The kinetics of nitrite (NO₂^––N) accumulation during denitrification was investigated using the Michaelis-Menten equation, and it was found that low concentrations of NO₃^––N had a more significant effect on the accumulation of NO₂^––N during denitrification. Organic matter was a key factor to regulate the synergy of anammox and denitrification, and altered the nitrogen removal pathways. The competition for NO₂^––N caused by high COD concentration was a crucial factor that affecting the system stability. Illumina sequencing techniques demonstrated that excess organic matter promoted the relative abundance of the Denitratesoma genus and the nitrite reductase gene nirS, causing the denitrifying bacteria Denitratisoma to compete with Cadidatus Kuenenia for NO₂^––N, thereby affecting the stability of the system. Synergistic carbon and nitrogen removal between partial denitrifiers and anammox bacteria can be effectively achieved by controlling the COD and COD/NO₃^––N.