Pollution Characteristics Research Articles

Heavy metal levels of outdoor dust from the Eastern Mediterranean Sea region and assessment of the ecological and health risk.

As a result of some chemical element (heavy metals) pollution of dust, environmental pollution of dust has become an increasing concern, necessitating an assessment of risks to both ecology and human health, particularly in urban areas. Most of these pollutants settle on the outdoors and eventually become part of the outdoor dust. These will have negative long-term repercussions on ecosystems and human health. In this research, energy dispersive X-ray fluorescence (EDXRF) spectrometry analytical method was used to assess the pollution characteristics of the eight heavy metals (HMs): Mn, Cu, As, Hg, Ni, Cr, Zn, and Pb in the East Mediterranean Sea area. The concentration of As, Mn, Cr, Cu, Hg, Ni, Pb, and Zn analyzed in outdoor dust samples varied from 0.94 to 19.52 mg kg-1, 190.08 to 1019.7 mg kg-1, 20.46 to 45.9 mg kg-1, 19.5 to 62.56 mg kg-1, 0.01 to 0.93 mg kg-1, 10.48 to 40.64 mg kg-1, 12. 6 to 36.1 mg kg-1, and 48.96 to 112.41mg kg-1, respectively. HMs have been detected in the outdoor dust samples analyzed in the study, and, as a result, mean concentrations followed the order Mn > Zn > Cu > Cr > Ni > Pb > As > Hg, respectively. The ecological risk was observed at various contamination levels, with As and Hg pollution being the most severe. The highest hazard quotient (HQ) for adults and children was determined as a result of As and Cr, respectively. According to the US-EPA health risk threshold, the cancer risk in the study area is negligible.

Distribution and Risk Assessment of VOCs in Road Dust Across Different Urban Areas

ABSTRACT Volatile organic compounds (VOCs) are significant pollutants in urban road dust, but their distribution and associated health risks remain unclear. VOC distribution in road dust from six distinct urban areas in Beijing Daxing District, China was systematically investigated. Eighteen VOCs were detected, with the highest load in commercial areas (7.73 μg/g), followed by industrial parks (6.38 μg/g), residential areas (3.35 μg/g), leisure areas (2.79 μg/g), traffic areas (2.01 μg/g) and urban-rural fringe (1.85 μg/g), much lower than in air and water phases. Typical VOCs in road dust include methyl tertiary butyl ether (MTBE) (0.82–1.24 μg/g), benzene series compounds (0.02–4.19 μg/g) and volatile halogenated alkanes (0.002-0.095 μg/g). Incomplete combustion of road vehicle fuels is the key source of VOCs. Particular attention was given to VOCs in typical sites within the urban-rural fringe, known for their unique pollution characteristics. The VOCs loads were ranked as follows: Gas Stations (2.61–2.62 μg/g) > Intersection (1.41 μg/g) > Main Road (1.34 μg/g) > University Gate (1.27 μg/g). Increased vehicle activity and frequent stalling contribute to higher VOCs accumulation in road dust. Health risk assessment indicates there is no appreciable non-carcinogenic risk associated with exposure to road dust VOCs. Further research should consider VOCs in the broader environmental context, including air and water.

Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Ambient temperature and the risk of childhood epilepsy hospitalizations: Potentially neglected risk of temperature extremes and modifying effects of air pollution

PurposeExtreme temperatures and air pollution are increasingly important risk factors for human health in the background of climate change, with limited evidence available for neurological disorders. This study intended to investigate the short-term effects of extreme temperatures on childhood epilepsy and explore the potential modifying effect of air pollution. MethodsDaily childhood epilepsy hospitalization, meteorological and air pollution data were collected from 10 cities in Anhui Province of China during 2016–2018. We firstly employed a space–time-stratified case-crossover design and conditional logistic regression model to fit the short-term relationship between temperature and epilepsy. Then, we conducted stratified analyses by the level of air pollution and individual characteristics. ResultsBoth extreme heat and extreme cold increased the risk of hospitalization for childhood epilepsy. The effect of extreme heat [97.5th vs. minimum hospitalization temperature (MHT)] on hospitalization was acute and emerged at lag0 [OR: 1.229 (95 %CI: 1.035 to 1.459)], while the effect of extreme cold (2.5th vs. MHT) was delayed and appeared at lag5 [OR: 1.098 (95 %CI: 1.043 to 1.156)]. We also found children aged 6–18 years were more susceptible to extreme cold than children aged 0–5 years. Besides, extreme heat and cold effects differed by the level of air pollutants. ConclusionThis study suggests that extreme temperatures might be the novel but currently neglected risk factor for childhood epilepsy, and air pollution could further amplify the adverse effect of temperature.

Effects of Composted Straw, Biochar, and Polyacrylamide Addition on Soil Permeability and Dynamic Leaching Characteristics of Pollutants in Loessial Soil in Urban Greenbelts According to Indoor Simulation Experiments

Effects of Composted Straw, Biochar, and Polyacrylamide Addition on Soil Permeability and Dynamic Leaching Characteristics of Pollutants in Loessial Soil in Urban Greenbelts According to Indoor Simulation Experiments

Quantitative expression of LNAPL pollutant concentrations in capillary zone by coupling multiple environmental factors based on random forest algorithm

The capillary zone plays a crucial role in migration and transformation of pollutants. Light nonaqueous liquids (LNAPLs) have become the main organic pollutant in soil and groundwater environments. However, few studies have focused on the concentration distribution characteristics and quantitative expression of LNAPL pollutants within capillary zone. In this study, we conducted a sandbox-migration experiment using diesel oil as a typical LNAPL pollutant, with the capillary zone of silty sand as the research object. The variation characteristics of LNAPL pollutants (total petroleum hydrocarbon) concentration and environmental factors (moisture content, electrical conductivity, pH, and oxidationreduction potential) were essentially consistent at different locations with the same height. These characteristics differed within range of 10.0–50.0 cm and above 60.0 cm from groundwater. A model for quantitative expression of concentrations was constructed by coupling multiple environmental factors of 968 sets-7744 data via random forest algorithm. The goodness of fit (R2) for both training and test sets was greater than 0.90, and the mean absolute percentage error (MAPE) was less than 16.00 %. The absolute values of relative errors in predicting concentrations at characteristic points were less than 15.00 %. The constructed model can accurately and quantitatively express and predict concentrations in capillary zone.

Analysis of Pollution Characteristics and Contributions of Firework Burnings in Nanchang during the Spring Festival.

This study investigates the impact of fireworks on air quality during the Spring Festival in Nanchang City, utilizing high-resolution monitoring data from February 7th to 15th, 2021. Significant variations in K+ concentrations were observed, indicating severe air quality impacts. During the most intense discharge event A, K+ concentrations were 20.7 times higher than background levels, with PM2.5 and PM10 levels rising to 3.63 and 3.32 times above the background, respectively. The contribution of fireworks to PM2.5 was determined to be 72.5 ± 25.6%. Sulfate (SO4 2-) and nitrate (NO3 -) concentrations also increased significantly, with Δ[SO4 2- ] and Δ[NO3 - ] accounting for 15.4 ± 18.7% and 10.9 ± 12.3% of PM2.5, respectively. The study highlights the necessity for effective emission control strategies to mitigate the adverse effects of fireworks on urban air quality and public health. Future research should focus on the detailed chemical pathways and long-term impacts of these episodic emissions.

Spatial-temporal distribution characteristics of surface water pollutants and their potential sources in Ngari, China.

The Ngari region has many important rivers and is critical to water resource security and water resource continuity in China and even in adjoining Asian countries. However, the spatial distribution and monthly variation in local water quality have been poorly understood until recently. In this study, the spatial-temporal variations of 12 water quality parameters, including pH, dissolved oxygen (DO), permanganate index (IMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NNH3), total nitrogen (Ntotal), total phosphorus (Ptotal), copper (Cu), fluoride (F), arsenic (As) and cadmium (Cd), were determined from samples collected monthly at 22 water cross-sectional sites in the Ngari region in 2020. The surface water pollution in the southern Ngari region was the most serious, and the water pollution level in winter was higher than that in the other seasons. As (0.0781 ~ 0.6154 mg/L) and F (1.05 ~ 4.64 mg/L) were the main exceedance factors derived from the recharge of high arsenic and fluoride geothermal water and weathering of As and F-bearing minerals. The hazard quotient and carcinogenic risk for As and F at the five contaminated sampling sites indicated potential health risks and even carcinogenicity to local populations. The hydrochemistry types of the lakes and rivers in the Ngari region were mainly chloride water and carbonate water. The results from this study can provide a scientific basis for the prevention and control of surface water pollution in the Ngari region and contribute to subsequent research on the ecology of water bodies.

Vertical distribution and transport characteristics of ozone pollution based on lidar observation network and data assimilation over the Pearl River Delta, China

In recent years, China has made certain progresses regarding the monitoring of ozone (O3) pollution. However, a comprehensive understanding of the three-dimensional distribution and evolutionary characteristics of O3 is still relatively lacking. This study analyzed an O3 pollution episode in the Pearl River Delta during September to October 2019 using data from five O3 lidars and air pollution monitoring stations combined with the Gridpoint Statistical Interpolation data assimilation system. The model's performance in the vertical direction improved after assimilation, with the correlation coefficient of model simulations improving by 87 %, errors decreasing by 30 %, and simulation accuracy increasing by 29 %. Results showed that O3 pollution persisted for more than a week due to high temperatures and the influence of Typhoon Mita. However, after October 2, the concentration of O3 decreased under the combined effects of southerly winds and reduced traffic emissions during the National Day period. Besides, lidar observations indicate that residual O3 at night can disrupt its typical diurnal variation patterns. Moreover, O3 pollution occurred both at ground level and greater heights. Overall, O3 is primarily produced by photochemical reactions. Nevertheless, the O3 concentration was also affected by pollutant transport from northeast to southwest at a height of 0–3 km during the pollution period, with an average transport flux of 366 μg·m−2·s−1. Anomalously high nighttime O3 concentrations (>100 μg·m−3) were observed in both coastal and inland cities. Residual O3 could lead to persistent pollution the next day. The results clarify the vertical distribution and evolutionary characteristics of O3, which is crucial for devising more targeted pollution control strategies in the Pearl River Delta.

Analysis of pollutant diffusion characteristics with intersection angle of 45° in environmental open channel

Analysis of pollutant diffusion characteristics with intersection angle of 45° in environmental open channel

Occurrence and distribution of legacy and novel brominated flame retardants in river and sediments in southwest China: A seasonal investigation

Brominated flame retardants (BFRs) and their substitutes are prevalent in the environment, especially near industrial point sources. In non-point source pollution areas, it is crucial to investigate the seasonal pollution characteristics to identify the pollution sources. In this study, compositional profiles, seasonal variations, and ecological risks of legacy BFRs and novel BFRs (NBFRs) in the water and sediment from the Tuojiang River located in southwest China were investigated. The results indicated that ΣBFRs ranged from not detected (n.d.) to 42.0 ng/L in water and from 0.13 to 17.6 ng/g in sediment, while ΣNBFRs ranged from n.d. to 15.8 ng/L in water, and from 0.25 to 6.82 ng/g in sediment. A significant seasonal variation was observed in water and sediments with high proportions of legacy BFRs (median percentage of 68.8% and 51.3% in water and sediment) in the dry season, while NBFRs (median percentage of 53.2% and 71.6% in water and sediment) exhibited predominance in the wet season. This highlighted the importance of surface runoff and atmospheric deposition as important sources of NBFRs in aquatic environments. Moreover, there were high ratios of decabromodiphenyl ethane (DBDPE) and BDE-209 (average: 1.38 and 2.76 in dry and wet season) in sediments adjacent to the residual areas, indicating a consumption shift from legacy BFRs to NBFRs in China. It was observed that legacy BFRs showed higher ecological risks compared to NBFRs in both water and sediment environments, with BDE-209 posing low to medium risks to sediment organisms. This study provides better understanding of contamination characteristics and sources of legacy BFRs and NBFRs in non-point source pollution areas.

Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach

Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distinct techniques: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning network of Long Short-Term Memory (LSTM). The objective is to characterize the variation in OCPs concentrations with high precision. Results show that the hybrid two-stage decomposition coupled models achieved an average symmetric mean absolute percentage error (SMAPE) of 23.24 % in the empirical analysis of typical surface water. It exhibited higher predictive power than the given individual benchmark models, which yielded an average SMAPE of 40.88 %, and single decomposition coupled models with an average SMAPE of 29.80 %. The proposed CEEMDAN-VMD-LSTM model, with an average SMAPE of 13.55 %, consistently outperformed the other models, yielding an average SMAPE of 33.53 %. A comparative analysis with shallow neural network methods demonstrated the advantages of the LSTM algorithm when coupled with secondary decomposition techniques for processing time series datasets. Furthermore, the interpretable analysis derived by the SHAP approach revealed that precipitation followed by the total phosphorus had strong effects on the predicted concentration of OCPs in the given water. The data presented herein shows the effectiveness of decomposition technique-based deep learning algorithms in capturing the dynamic characteristics of pollutants in surface water.

Characteristic Analysis and Health Risk Assessment of PM2.5 and VOCs in Tianjin Based on High-Resolution Online Data.

This study leveraged 2019 online data of particulate matter (PM2.5) and volatile organic compounds (VOCs) in Tianjin to analyze atmospheric pollution characteristics. PM2.5 was found to be primarily composed of water-soluble ions, with nitrates as the dominant component, while VOCs were predominantly alkanes, followed by alkenes and aromatic hydrocarbons, with notable concentrations of propane, ethane, ethylene, toluene, and benzene. The receptor model identified six major sources of PM2.5 and seven major sources of VOCs. The secondary source is the main contribution source, while motor vehicles and coal burning are important primary contribution sources in PM2.5. And, industrial processes and natural gas volatilization were considered major contributors for VOCs. A health risk assessment indicated negligible non-carcinogenic risks but potential carcinogenic risks from trace metals As and Cr, and benzene within VOCs, underscoring the necessity for focused public health measures. A risk attribution analysis attributed As and Cr in PM to coal combustion and vehicular emissions. Benzene in VOCs primarily originates from fuel evaporation, and industrial and vehicular emissions. These findings underscore the potential for reducing health risks from PM and VOCs through enhanced regulation of emissions in coal, industry, and transportation. Such strategies are vital for advancing air quality management and safeguarding public health.

Machine learning model to predict rate constants for sonochemical degradation of organic pollutants

In this study, machine learning (ML) algorithms were employed to predict the pseudo-1st-order reaction rate constants for the sonochemical degradation of aqueous organic pollutants under various conditions. A total of 618 sets of data, including ultrasonic, solution, and pollutant characteristics, were collected from 89 previous studies. Considering the difference between the electrical power (Pele) and calorimetric power (Pcal), the collected data were divided into two groups: data with Pele and data with Pcal. Eight input variables, including frequency, power density, pH, temperature, initial concentration, solubility, vapor pressure, and octanol–water partition coefficient (Kow), and one target variable of the degradation rate constant, were selected for ML. Statistical analysis was conducted, and outliers were determined separately for the two groups. ML models, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were used to predict the pseudo-1st-order reaction rate constants for the removal of aqueous pollutants. The prediction performance of the ML models was evaluated using different metrics, including the root mean squared error (RMSE), mean absolute error (MAE), and R squared (R2). A significantly higher prediction performance was obtained using data without outliers and augmented data. Consequently, all the applied ML models could be used to predict the sonochemical degradation of aqueous pollutants, and the XGB model showed the highest accuracy in predicting the rate constants. In addition, the power density and frequency were the most influential factors among the eight input variables in prediction with the Shapley additive explanation (SHAP) values method. The degradation rate constants of the two pollutants over a wide frequency range (20–1,000 kHz) were predicted using the trained ML model (XGB) and the prediction results were analyzed.

Integrated Analysis of Pollution Characteristic and Ecotoxicological Effect Reveals the Fate of Lithium in Soil-Plant Systems: A Challenge to Global Sustainability.

Lithium, as an emerging contaminant, lacks sufficient information regarding its environmental and ecotoxicological implications within soil-plant systems. Employing maize, wheat, pea, and water spinach, we conducted a thorough investigation utilizing a multispecies, multiparameter, and multitechnique approach to assess the pollution characteristics and ecotoxicological effects of lithium. The findings suggested that lithium might persist in an amorphous state, altering surface functional groups and chemical bonds, although semiquantitative analysis was unattainable. Notably, lithium demonstrated high mobility, with a mild acid-soluble fraction accounting for 29.66-97.02% of the total, while a minor quantity of exogenous lithium tended to be a residual fraction. Plant analysis revealed that in 10-80 mg Li/kg soils lithium significantly enhanced certain growth parameters of maize and pea, and the calculated LC50 values for aerial part length across the four plant species varied from 173.58 to 315.63 mg Li/kg. Lithium accumulation in the leaves was up to 1127.61-4719.22 mg/kg, with its inorganic form accounting for 18.60-94.59%, and the cytoplasm fraction (38.24-89.70%) predominantly harbored lithium. Furthermore, the model displayed that growth stimulation might be attributed to the influence of lithium on phytohormone levels. Water spinach exhibited superior accumulation capacity and tolerance to lithium stress and was a promising candidate for phytoremediation strategies. Our findings contribute to a more comprehensive understanding of lithium's environmental behavior within soil-plant systems, particularly within the context of global initiatives toward carbon neutrality.

Pollution Characteristics of Heavy Metals in PM1 and Source-Specific Health Risks in the Tianjin Airport Community, China.

The airport and its surrounding areas are home to a variety of pollution sources, and air pollution is a recognized health concern for local populated regions. Submicron particulate matter (PM1 with an aerodynamic diameter of <1 mm) is a typical pollutant at airports, and the enrichment of heavy metals (HMs) in PM1 poses a great threat to human health. To comprehensively assess the source-specific health effects of PM1-bound HMs in an airport community, PM1 filter samples were collected around the Tianjin Binhai International Airport for 12 h during the daytime and nighttime, both in the spring and summer, and 10 selected HMs (V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) were analyzed. The indicatory elements of aircraft emissions were certified as Zn and Pb, which accounted for more than 60% of the sum concentration of detected HMs. The health risks assessment showed that the total non-cancer risks (TNCRs) of PM1-bound HMs were 0.28 in the spring and 0.23 in the summer, which are lower than the safety level determined by the USEPA, and the total cancer risk (TCR) was 2.37 × 10-5 in the spring and 2.42 × 10-5 in the summer, implying that there were non-negligible cancer risks in the Tianjin Airport Community. After source apportionment with EF values and PMF model, four factors have been determined in both seasons. Consequently, the source-specific health risks were also evaluated by combining the PMF model with the health risk assessment model. For non-cancer risk, industrial sources containing high concentrations of Mn were the top contributors in both spring (50.4%) and summer (44.2%), while coal combustion with high loads of As and Cd posed the highest cancer risk in both seasons. From the perspective of health risk management, targeted management and control strategies should be adopted for industrial emissions and coal combustion in the Tianjin Airport Community.

Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations

During the Lunar New Year of 2020, the Chinese government implemented a strict nationwide lockdown to prevent the spread of the COVID-19, which may have led to changes in the distribution of pollutants. To enhance our understanding of the three-dimensional evolution of pollutants under special control scenarios, this study used vehicle lidar to obtain vertical profiles of aerosols and ozone (O3) in Wuhan during the pandemic. Combined with ground observations and WRF-Chem model simulation results, we explored the evolution characteristics of pollutants. The results showed that the fine particulate matter (PM2.5) concentration near the ground in Wuhan City was significantly reduced (40%) during the emission control period. The aerosol concentration decreased rapidly with an increase in height, and the maximum loading height was consistent with the atmospheric boundary layer height. Conversely, due to factors such as the reduction of the precursor nitrogen dioxide (NO2) and rising temperatures, the near-ground O3 concentration increased by 59%. Despite the significant reduction in emission sources, lidar observations still captured distinct aerosol transport layers on March 7 and March 10, 2020. These transport layers gradually descended, affecting the ground-level aerosol concentration. Additionally, one typical process of O3 external transport was observed in the vertical direction. Our study provides a theoretical basis for a deeper understanding of the spatiotemporal distribution characteristics of pollutants during special control scenarios, aiding policymakers in formulating effective air pollution control strategies.

Spatial Dynamic Interaction Effects and Formation Mechanisms of Air Pollution in the Central Plains Urban Agglomeration in China

Accurately identifying the dynamic interaction effects and network structure characteristics of air pollution is essential for effective collaborative governance. This study investigates the spatial dynamic interactions of air pollution among 30 cities in the Central Plains Urban Agglomeration using convergent cross mapping. Social network analysis is applied to assess the overall and node characteristics of the spatial interaction network, while key driving factors are analyzed using an exponential random graph model. The findings reveal that air pollution levels in the Central Plains Urban Agglomeration initially increase before they decrease, with heavily polluted cities transitioning from centralized to sporadic distribution. Among the interactions, Heze’s air pollution impact on Kaifeng was the strongest, while Xinxiang’s impact on Changzhi was the weakest. The emission and receiving effects peaked during 2010–2012. The air pollution interactions among cities exhibit significant network characteristics, with block model results indicating that emitting and receiving relationships are primarily concentrated in the bidirectional spillover plate. Natural factors such as temperature and precipitation significantly influence the spatial interaction network. Economic and social factors like economic level and industrial sector proportion also have a significant impact. However, population density does not influence the spatial interaction network. This study contributes to understanding the spatial network of air pollution, thereby enhancing strategies for optimizing regional collaborative governance efforts to address air pollution.

Polycyclic aromatic hydrocarbons in fish from four lakes in central and eastern China: Bioaccumulation, pollution characteristics, sources, and health risk assessment

Polycyclic aromatic hydrocarbons (PAHs) are highly concerning environmental pollutants due to their toxicity, persistence, and bioaccumulation. In this paper, concentrations and compositions of 16 United States Environmental Protection Agency (USEPA) priority control PAHs in the fish collected from four lakes in central and eastern China were analyzed. 18 species of fish were collected from four typical lakes, namely Taihu Lake, Danjiangkou Reservoir, Yuncheng Salt Lake, and Nansi Lake. Quantitative analysis of PAHs were carried out with gas chromatograph/mass spectrometer, and 13 out of 16 PAHs were identified, with the main components being pyrene, chrysene, naphthalene, and benzo(b)fluoranthene. The accumulation of PAHs in fish from Taihu Lake, Danjiangkou Reservoir, Yuncheng Salt Lake, and Nansi Lake was 28.75–47.27, 26.60–31.93, 33.56–39.30, and 27.22–43.01 ng·g−1, respectively. The toxic equivalents of high-cyclic PAHs in fish of the four lakes were significantly higher than those of low-cyclic and middle-cyclic PAHs (P < 0.05). In Taihu Lake, Danjiangkou Reservoir, and Nansi Lake, the toxicity equivalents were predominantly contributed by benzo[a] pyrene (BaP), while in Yuncheng Salt Lake, dibenzo(a,h) anthracene (DahA) was the main contributor. The residents in central and eastern China exposed to PAHs had a negligible non-cancer risk (non-carcinogenic risk values <1) and a potential low cancer risk. It was noteworthy that the Pleuronichthys cornutus and Lateolabrax japonicus from Yuncheng Salt Lake could pose carcinogenic risks (>10−4) to humans, with benzo[b]fluoranthene (BbF) having the highest risk contribution rate. Source analysis indicated that the main source of PAHs in fish was combustion sources. BaP, DahA, and BbF could become potential pollutants of concern in the field of ecotoxicology. The results of this study on PAHs bioaccumulation, pollution characteristics, sources and health risks in fish from four lakes would provide a scientific basis for local governments to formulate targeted environmental management policies, pollution control measures, and public health strategies.

Study on main sources of aerosol pH and new methods for additional reduction of PM2.5 during winter severe pollution: Based on the PMF-GAS model

In recent years, the air quality in urban areas of China has been a significant improvement. However, further advancements in air quality remain challenging, particularly in addressing the persistent problem of severe particulate matter (PM) pollution in winter. In-depth research on the aerosol acidity (pH) is beneficial for analyzing the pollution characteristics of PM and providing support for the continuous improvement of PM pollution. In this study, the sources of water-soluble inorganic ions (WSIIs) of PM2.5 in Zhengzhou were analyzed based on the PMF-GAS method, and the contribution of different sources to aerosol acidity was further investigated in conjunction with the ISORROPIA-II thermodynamic model. The research results indicate that secondary aerosols are the main source, and controlling the emissions of secondary aerosols and gaseous precursors is a crucial measure for reducing PM2.5 concentrations. Furthermore, the study revealed that targeted reductions of major pollution sources across varying pH ranges could significantly lower PM2.5 concentrations. In the range of pH < 4, when the contribution of secondary aerosol source is 23.9%, PM2.5 concentrations can be reduced by 40.6%, resulting in an additional emission reduction of 5.9 μg m−3. In the range of 4 < pH < 6, PM2.5 can be reduced by 36.9% at a contribution of 16.5% from secondary aerosol source, achieving an additional emission reduction of 7.0 μg m−3. This research provides important theoretical support for gaining a deeper understanding of the sources and control of atmospheric pollution, offering strategies to expedite the achievement of PM2.5 standards and further mitigating its impacts on human health and the environment.