Pollution Control Strategies Research Articles

Modern environmental art design based on Artificial Intelligence technology and ecological civilization

This study delves into a novel approach for energy conservation and environmental pollution reduction through modern environmental art design, guided by the ecological civilization concept and powered by artificial intelligence (AI) technology. The environmental art framework, aligning with the ecological civilization paradigm, is intricately designed. The data acquisition layer employs diverse sensors to gather equipment status, environmental, and pollution data, transmitting it to the executive controller layer via internal WIFI connectivity. The collected data undergoes meticulous analysis and processing within the data layer before reaching the actuator control layer. Leveraging support vector machines in artificial intelligence, the executive controller layer amalgamates the analyzed equipment and environmental data to devise energy-saving equipment and environmental pollution control schemes. Real-time visualization of these outcomes is achieved through the display operation layer. Findings affirm the effectiveness of this method in acquiring pertinent data for modern environmental art design and managing equipment states. Implementation of this approach successfully diminishes power consumption, dust concentration, and formaldehyde levels in the modern environmental art design zone, showcasing its prowess in energy conservation and pollution control. The integration of AI within the ecological civilization framework highlights its potential in fostering sustainable and environmentally conscious practices in modern art creation.

Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements

Fine particulate matter (PM2.5) causes millions of premature deaths each year worldwide. Oxidative potential (OP) has been proposed as a better metric for aerosol health effects than PM2.5 mass concentration alone. In this study, we report for the first time online measurements of PM2.5 OP in wintertime Beijing and surroundings based on a dithiothreitol (DTT) assay. These measurements were combined with co-located PM chemical composition measurements to identify the main source categories of aerosol OP. In addition, we highlight the influence of two distinct pollution events on aerosol OP (spring festival celebrations including fireworks and a severe regional dust storm). Source apportionment coupled with multilinear regression revealed that primary PM and oxygenated organic aerosol (OOA) were both important sources of OP, accounting for 41 ± 12 % and 39 ± 10 % of the OPvDTT (OP normalized by the sampled air volume), respectively. The small remainder was attributed to fireworks and dust, mainly resulting from the two distinct pollution events. During the 3.5-day spring festival period, OPvDTT spiked to 4.9 nmol min−1 m−3 with slightly more contribution from OOA (42 ± 11 %) and less from primary PM (31 ± 15 %). During the dust storm, hourly-averaged PM2.5 peaked at a very high value of 548 μg m−3 due to the dominant presence of dust-laden particles (88 % of total PM2.5). In contrast, only mildly elevated OPvDTT values (up to 1.5 nmol min−1 m−3) were observed during this dust event. This observation indicates that variations in OPvDTT cannot be fully explained using PM2.5 alone; one must also consider the chemical composition of PM2.5 when studying aerosol health effects. Our study highlights the need for continued pollution control strategies to reduce primary PM emissions, and more in-depth investigations into the source origins of OOA, to minimize the health risks associated with PM exposure in Beijing.

Long-term exposure to fine particulate matter constituents in relation to chronic kidney disease: evidence from a large population-based study in China.

The effects of long-term exposure to fine particulate matter (PM2.5) constituents on chronic kidney disease (CKD) are not fully known. This study sought to examine the association between long-term exposure to major PM2.5 constituents and CKD and look for potential constituents contributing substantially to CKD. This study included 81,137 adults from the 2018 to 2019 baseline survey of China Multi-Ethnic Cohort. CKD was defined by the estimated glomerular filtration rate. Exposure concentration data of 7 major PM2.5 constituents were assessed by satellite remote sensing. Logistic regression models were used to estimate the effect of each PM2.5 constituent exposure on CKD. The weighted quantile sum regression was used to estimate the effect of mixed exposure to all constituents. PM2.5 constituents had positive correlations with CKD (per standard deviation increase), with ORs (95% CIs) of 1.20 (1.02-1.41) for black carbon, 1.27 (1.07-1.51) for ammonium, 1.29 (1.08-1.55) for nitrate, 1.20 (1.01-1.43) for organic matter, 1.25 (1.06-1.46) for sulfate, 1.30 (1.11-1.54) for soil particles, and 1.63 (1.39-1.91) for sea salt. Mixed exposure to all constituents was positively associated with CKD (1.68, 1.32-2.11). Sea salt was the constituent with the largest weight (0.36), which suggested its importance in the PM2.5-CKD association, followed by nitrate (0.32), organic matter (0.18), soil particles (0.10), ammonium (0.03), BC (0.01). Sulfate had the least weight (< 0.01). Long-term exposure to PM2.5 sea salt and nitrate may contribute more than other constituents in increasing CKD risk, providing new evidence and insights for PM2.5-CKD mechanism research and air pollution control strategy.

Hyperparameter optimization for hourly PM2.5 pollutant prediction

Air pollution, particularly the presence of Particulate Matter (PM) 2.5, poses significant health risks to humans, with industrial growth and urban vehicle emissions being major contributors. This study utilizes machine learning techniques, specifically K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, to predict PM2.5 levels. A dataset from Kaggle consisting of PM2.5 and other pollutant parameters is preprocessed and split into training and testing sets. The models are trained, evaluated, and compared using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Additionally, hyperparameters are applied to optimize the models. Results show that SVM with hyperparameters performs better, indicating its potential for accurate air quality prediction. These findings can aid policymakers in implementing effective pollution control strategies.

Weakened aerosol–radiation interaction exacerbating ozone pollution in eastern China since China's clean air actions

Abstract. Since China's clean air action, PM2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less) air quality has improved, while ozone (O3) pollution has become more severe. Here we apply a coupled meteorology–chemistry model (WRF-Chem: Weather Research and Forecasting model coupled to Chemistry v3.7.1) to quantify the responses of aerosol–radiation interaction (ARI) to anthropogenic emission reductions from 2013 to 2017, including aerosol–photolysis interaction (API) related to photolysis rate change and aerosol–radiation feedback (ARF) related to meteorological field change and their contributions to O3 increases over eastern China in summer and winter. Sensitivity experiments show that the decreased anthropogenic emissions play a more prominent role in the increased daily maximum 8 h average (MDA8) O3 in both summer (+1.96 ppb vs. +0.07 ppb) and winter (+3.56 ppb vs. −1.08 ppb) than the impacts of changed meteorological conditions in urban areas. The decreased PM2.5 caused by emission reductions can result in a weaker impact of ARI on O3 concentrations, which superimposes its effect on the worsened O3 air quality. The weakened ARI due to decreased anthropogenic emissions aggravates the summer (winter) O3 pollution by +0.81 ppb (+0.63 ppb), averaged over eastern China, with weakened API contributing 55.6 % (61.9 %) and ARF contributing 44.4 % (38.1 %), respectively. This superimposed effect is more significant for urban areas during summer (+1.77 ppb). Process analysis indicates that the enhanced chemical production is the dominant process for the increased O3 concentrations caused by weakened ARI in both summer and winter. This study innovatively reveals the adverse effect of weakened aerosol–radiation interaction due to decreased anthropogenic emissions on O3 air quality, indicating that more stringent coordinated air pollution control strategies should be implemented for significant improvements in future air quality.

Chemical engineering and the circular water economy: Simulations for sustainable water management in environmental systems

In the face of escalating water scarcity and environmental degradation, the imperative for sustainable water management has never been more urgent. Chemical engineering emerges as a pivotal discipline in the pursuit of solutions to these pressing challenges. This review explores the role of chemical engineering in advancing the circular water economy paradigm through simulations aimed at fostering sustainable water management within environmental systems. The circular water economy concept advocates for the efficient utilization, recycling, and reclamation of water resources to minimize waste and maximize resource efficiency. Chemical engineering techniques play a fundamental role in realizing this vision through the design and optimization of water treatment processes, resource recovery systems, and advanced simulation methodologies. This review delves into the application of computational simulations within the realm of chemical engineering to model and analyze various aspects of the water cycle. Such simulations enable the assessment of complex environmental systems, aiding in the identification of optimal strategies for water resource allocation, pollution control, and ecosystem preservation. Key areas of focus include the simulation of wastewater treatment processes, such as biological, physical, and chemical treatment methods, to enhance pollutant removal efficiency and promote water reuse. Furthermore, advanced modeling techniques facilitate the evaluation of innovative technologies like membrane filtration, adsorption, and electrochemical processes for the purification and desalination of water resources. Moreover, chemical engineering simulations enable the assessment of integrated water management strategies, encompassing aspects of urban water systems, industrial processes, and agricultural practices. By considering the interconnectedness of various sectors, holistic approaches to water resource management can be formulated, promoting resilience and sustainability in the face of changing environmental conditions. The integration of chemical engineering simulations into the framework of the circular water economy offers a promising avenue for advancing sustainable water management practices. Through comprehensive modeling and analysis, informed decision-making can pave the way towards a more resilient and equitable water future, ensuring the long-term viability of our environmental systems.

Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model

PM2.5 constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition costs, and multiple uncertainties. These limitations hinder the acquisition of comprehensive information on PM2.5 chemical composition and effectively implement refined air pollution protection and control strategies. In this study, we developed an optimal deep learning model to acquire hourly mass concentrations of key PM2.5 chemical components without complex chemical analysis. The model was trained using a randomly partitioned multivariate dataset arranged in chronological order, including atmospheric state indicators, which previous studies did not consider. Our results showed that the correlation coefficients of key chemical components were no less than 0.96, and the root mean square errors ranged from 0.20 to 2.11 µg/m3 for the entire process (training and testing combined). The model accurately captured the temporal characteristics of key chemical components, outperforming typical machine-learning models, previous studies, and global reanalysis datasets (such as Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA)). We also quantified the feature importance using the random forest model, which showed that PM2.5, PM1, visibility, and temperature were the most influential variables for key chemical components. In conclusion, this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data, improved air pollution monitoring and source identification. This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.

Smart city air quality management through leveraging drones for precision monitoring

Air pollution stands out as one of the most critical challenges impacting public health, the overall quality of life, and the pursuit of sustainable development in urban environments. Each existing air quality monitoring method carries inherent limitations alongside its advantages. This study addresses the drawbacks of both satellite-based and fixed monitoring stations by proposing a drone-based air quality monitoring solution. The entire urban area was surveyed using a multi-rotor drone equipped with sensors capable of measuring atmospheric components, resulting in the creation of high-resolution air pollution maps. The expansive urban area, exceeding the coverage capacity of a single flight, was systematically subdivided into subregions. Distinct measurements were conducted in each subregion, and a consolidated mapping was generated by combining the individual results obtained. The drone, operating autonomously, conducted precise measurements on a citywide scale, enhancing the consistency of pollutant mapping by eliminating potential errors associated with manual control. Pollution sources were identified throughout the city, and real-time data revealed how transportation-induced air pollution affected different regions. Environmental factors like wind and pressure differences were identified as influential in the varied spread of air pollutants across different parts of the city. The study underscores the pivotal role of transportation in urban air pollution, laying the groundwork for the development of targeted pollution control strategies in urban environments. Furthermore, the research validates the potential of UAVs in advancing the efficient management of smart cities, particularly through the monitoring and analysis of air pollution stemming from transportation.

3D multi-robot olfaction in naturally ventilated indoor environments: Locating a time-varying source at unknown heights

Source localization is significant for mitigating indoor air pollution and safeguarding the well-being and safety of occupants. While most study focuses on mechanical ventilation and static sources, this study explores the less-explored domain of locating time-varying sources in naturally ventilated spaces. We have developed an innovative 3D localization system that adjusts to varying heights, significantly enhancing capabilities beyond traditional fixed-height 2D systems. To ensure consistency in experimental conditions, we conducted comparative analyses of 2D and 3D methods, using a swinging fan to simulate natural ventilation. Our findings reveal a substantial disparity in performance: the 2D method had a success rate below 46.7% in cases of height mismatches, while our 3D methods consistently achieved success rates above 66.7%, demonstrating their superior effectiveness in complex environments. Furthermore, we validated the 3D strategies in real naturally ventilated settings, confirming their wider applicability. This research extends the scope of indoor source localization and offers valuable insights and strategies for more effective pollution control.

Influence of Transboundary Pollution on the Variability of Surface Ozone Concentrations in the Desert Southwest of the U.S.: Case Study for Arizona

Arizona, a rapidly growing state in the southwestern U.S., faces ozone pollution challenges, including nonattainment areas in Yuma and Maricopa counties influenced by neighboring state pollution transport. In this study, we use five-year (2017–2021) hourly back-trajectories and O3 concentration data for concentration-weighted trajectory (CWT) analysis to identify transport pathways and potential source regions of O3 at six monitoring sites in Arizona. We divide the data into five seasons (winter, spring, dry summer, monsoon summer, and fall) to examine variations in O3 concentration and transport across sites and seasons. The highest mean O3 concentrations occur during spring (37–49 ppb), dry summer (39–51 ppb), and monsoon summer (34–49 ppb), while winter (19–41 ppb) exhibits the lowest seasonal mean. The CWT results reveal that high O3 concentrations (≥40 ppb) in Arizona, with the exception of Phoenix and Tucson sites, are influenced significantly by regional and international transport, especially in spring (14.9–35.4%) and dry summer (12.7–26.9%). The major potential source areas (excluding the Phoenix and Tucson sites) are predominantly located outside Arizona. This study highlights the critical role of pollution transport in influencing O3 variability within Arizona and will be valuable in shaping pollution control strategies in the future.

Can aerosol optical depth unlock the future of air quality monitoring and lung cancer prevention?

AbstractThis “Policy Brief” explores the potential integration of Aerosol Optical Depth (AOD) into the United Kingdom’s air quality and public health monitoring frameworks, highlighting its potential to enhance existing air pollution control strategies. Amid growing concerns over air pollution’s impact on health, particularly the link between particulate matter and lung cancer, this brief presents a focused investigation into how AOD can be leveraged alongside traditional monitoring methods to provide a more nuanced understanding of air quality trends. By correlating AOD data with lung cancer incidence rates within the UK, the brief aims to uncover potential associations and inform public health decisions. Furthermore, it discusses the advantages and limitations of employing AOD in air pollution and respiratory disease monitoring, advocating for a strategic enhancement of the UK's air pollution monitoring efforts. This approach seeks to complement and refine current monitoring practices with advanced remote sensing techniques, aiming to inform policy innovations that prioritize environmental health and public welfare. Through a comprehensive review of existing data and policies, the brief underscores the urgency of adopting multidimensional air quality management strategies that respond to technological advancements and emerging public health needs.

An assessment of atmospheric concentrations and spatiotemporal variation of BTEX and associated pollutants in India

Benzene, toluene, ethylbenzene, and xylene (BTEX) pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects, carcinogenic properties, and adverse effects on air quality. BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities, necessitating the assessment of atmospheric concentrations and their spatial variation. This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India. The study investigates the correlation of BTEX with other criteria pollutants and meteorological parameters, aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions. Additionally, the paper categorizes various regions in India according to the Air Quality Index (AQI) based on BTEX pollution levels. The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central, eastern, and western regions. In contrast, the southern zone experiences the least pollution with BTEX. Seasonal analysis indicates that winter and post-monsoon periods, characterized by lower temperatures, are associated with higher BTEX levels due to the accumulation of localized emissions. When comparing the different zones in India, high traffic emissions and localized activities, such as solvent use and solvent evaporation, are found to be the primary sources of BTEX. The findings of the current study aid in source characterization and identification, and better understanding of the region's air quality problems, which helps in the development of focused BTEX pollution reduction and control strategies.

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019–2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2–98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Vertical Evolution of Ozone Formation Sensitivity Based on Synchronous Vertical Observations of Ozone and Proxies for Its Precursors: Implications for Ozone Pollution Prevention Strategies.

Photochemical ozone (O3) formation in the atmospheric boundary layer occurs at both the surface and elevated altitudes. Therefore, the O3 formation sensitivity is needed to be evaluated at different altitudes before formulating an effective O3 pollution prevention and control strategy. Herein, we explore the vertical evolution of O3 formation sensitivity via synchronous observations of the vertical profiles of O3 and proxies for its precursors, formaldehyde (HCHO) and nitrogen dioxide (NO2), using multi-axis differential optical absorption spectroscopy (MAX-DOAS) in urban areas of the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions in China. The sensitivity thresholds indicated by the HCHO/NO2 ratio (FNR) varied with altitude. The VOC-limited regime dominated at the ground level, whereas the contribution of the NOx-limited regime increased with altitude, particularly on heavily polluted days. The NOx-limited and transition regimes played more important roles throughout the entire boundary layer than at the surface. The feasibility of extreme NOx reduction to mitigate the extent of the O3 pollution was evaluated using the FNR-O3 curve. Based on the surface sensitivity, the critical NOx reduction percentage for the transition from a VOC-limited to a NOx-limited regime is 45-72%, which will decrease to 27-61% when vertical evolution is considered. With the combined effects of clean air action and carbon neutrality, O3 pollution in the YRD and PRD regions will transition to the NOx-limited regime before 2030 and be mitigated with further NOx reduction.

Association between ambient PM1 and the prevalence of chronic kidney disease in China: A nationwide study

Particulate of diameter ≤ 1 µm (PM1) presents a novel risk factor of adverse health effects. Nevertheless, the association of PM1 with the risk of chronic kidney disease (CKD) in the general population is not well understood, particularly in regions with high PM1 levels like China. Based on a nationwide representative survey involving 47,204 adults and multi-source ambient air pollution inversion data, the present study evaluated the association of PM1 with CKD prevalence in China. The two-year average PM1, particulate of diameter ≤ 2.5 µm (PM2.5), and PM1–2.5 values were accessed using a satellite-based random forest approach. CKD was defined as estimated glomerular filtration rate < 60 ml/min/1.73 m2 or albuminuria. The results suggested that a 10 μg/m3 rise in PM1 was related to a higher CKD risk (odds ratio [OR], 1.13; 95% confidence interval [CI] 1.08–1.18) and albuminuria (OR, 1.11; 95% CI, 1.05–1.17). The association between PM1 and CKD was more evident among urban populations, older adults, and those without comorbidities such as diabetes or hypertension. Every 1% increase in the PM1/PM2.5 ratio was related to the prevalence of CKD (OR, 1.03; 95% CI, 1.03–1.04), but no significant relationship was found for PM1–2.5. In conclusion, the present study demonstrated long-term exposure to PM1 was associated with an increased risk of CKD in the general population and PM1 might play a leading role in the observed relationship of PM2.5 with the risk of CKD. These findings provide crucial evidence for developing air pollution control strategies to reduce the burden of CKD.

Identification of groundwater pollution sources and health risk assessment in the Songnen Plain based on PCA-APCS-MLR and trapezoidal fuzzy number-Monte Carlo stochastic simulation model

The source of inorganic pollutants in the groundwater of the Songnen Plain are complex; therefore, it is urgent to analyze the pollution sources and their contribution rates, and to determine the effects of pollutants on human health. In this study, the solution for the insufficient extraction of characteristic information and subjective weight in principal component analysis (PCA) was found using log-centralization and the entropy weight method. Based on 896 groundwater samples, the types and distribution characteristics of pollution sources were identified, and the APCS-MLR receptor model was used to quantitatively characterize the contribution of pollution sources to water quality. Considering the uncertainty of the health risk assessment process, a trapezoidal fuzzy number-Monte Carlo stochastic simulation model (TFN-MCS) was constructed. Overcoming the inapplicability of triangular fuzzy numbers in dealing with highly discrete water quality data. The results showed that: (1) The five potential pollution sources in the Songnen Plain were salinity factor (34.086 %), agricultural pollution factor (15.854 %), geological background factor (13.11 %), industrial pollution factor (10.382 %) and organic pollution factor (7.382 %). The degree of groundwater pollution in the study area is controlled by the terrain, hydrogeological conditions, land use type, and industrial layout. (2) The APCS-MLR receptor model was suitable for the allocation of potential sources of groundwater pollution. The average contributions rate of the five common factors to groundwater pollution were 38.86, 14.12, 15.73, 14.6, and 6.77 %. Salinity factor plays a major role in groundwater quality. (3) The THI of children and adults were 2.15–15.03 and 0.86–7.261, respectively, much higher than the safety threshold of one. In general, the THI of children from ingesting contaminated groundwater is twice that to adults. NO3– and F- are the primary risk control indicators for groundwater in the study area. These results support the formulation of effective groundwater pollution control strategies.

Nitrate Pollution Characteristics and Its Quantitative Source Identification of Major River Systems in China

Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρ（NO3--N） in the seven major river systems was （4.54±3.99） mg·L-1， with 9.6% of river ρ（NO3--N） exceeding 10 mg·L-1. The riverine ρ（NO3--N） in eastern China were higher than that in western China， and the highest concentration was observed in the Haihe River system. Additionally， tributaries experienced more serious NO3--N pollution than that in the main stream. The ρ（NO3--N） in most river systems in the dry season was higher than that in the wet season， except in the Yellow River system. There was significant nitrification in the Pearl River system， the middle and lower reaches of the Yellow River system， the middle reaches of the Liaohe River system， the Songhua River system， and the Haihe River system， whereas there was significant denitrification in the Yangtze River system， the Huaihe River system， and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR model， the major NO3--N source was sewage/manure （ &gt; 50%） in the Yangtze River system， Haihe River system， Liaohe River system， and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system （56.4%）， and the contribution of fertilizer nitrogen， soil nitrogen， and sewage/manure to NO3--N pollution in the Pearl River system， Huai River system， and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main stream， whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogen， fertilizer nitrogen， and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry season， whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Therefore， point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River system， the Yangtze River system， the Liaohe River system， the tributaries and the downstream main stream areas of Yellow River system， and the downstream area of the Pearl River system， whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River system， the Songhua River system， the middle reaches of the main stream area of the Yellow River system， and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.

The Unique Fe3Mo3N Structure Bestowed Efficient Fenton-Like Performance of the Iron-Based Catalysts: The Double Enhancement of Radicals and Nonradicals.

Iron-based catalysts are widely used in Fenton-like water pollution control technology due to their high efficiency, but their practical applications are limited by complex preparation conditions and strong blockage of Fe2+/Fe3+ cycle during the reaction. Here, a new iron-molybdenum bimetallic carbon-based catalyst is designed and synthesized using cellulose hydrogel for adsorption of Fe and Mo bimetals as a template, and the effective iron cycle in water treatment is realized. The integrated materials (Fe2.5Mo@CNs) with "catalytic/cocatalytic" performance have higher Fenton-like activation properties and universality than the equivalent quantity iron-carbon-based composite catalysts (Fe@CNs). Through the different characterization methods, experimental verifications and theoretical calculations show that the unique Fe3Mo3N structure promotes the adsorption of persulfate and reduces the energy barrier of the reaction, further completing the double enhancement of radicals (such as SO4·-) and nonradicals (1O2 and electron transport process). The integrated "catalytic/cocatalytic" combined material is expected to provide a new promotion strategy for Fenton-like water pollution control.

The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2

Abstract. Sensitivity analysis in chemical transport models quantifies the response of output variables to changes in input parameters. This information is valuable for researchers engaged in data assimilation and model development. Additionally, environmental decision-makers depend upon these expected responses of concentrations to emissions when designing and justifying air pollution control strategies. Existing sensitivity analysis methods include the finite-difference method, the direct decoupled method (DDM), the complex variable method, and the adjoint method. These methods are either prone to significant numerical errors when applied to nonlinear models with complex components (e.g. finite difference and complex step methods) or difficult to maintain when the original model is updated (e.g. direct decoupled and adjoint methods). Here, we present the implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2 as CMAQ-hyd. CMAQ-hyd can be applied to compute numerically exact first- and second-order sensitivities of species concentrations with respect to emissions or concentrations. Compared to CMAQ-DDM and CMAQ-adjoint, CMAQ-hyd is more straightforward to update and maintain, while it remains free of subtractive cancellation and truncation errors, just as those augmented models do. To evaluate the accuracy of the implementation, the sensitivities computed by CMAQ-hyd are compared with those calculated with other traditional methods or a hybrid of the traditional and advanced methods. We demonstrate the capability of CMAQ-hyd with the newly implemented gas-phase chemistry and biogenic aerosol formation mechanism in CMAQ. We also explore the cross-sensitivity of monoterpene nitrate aerosol formation to its anthropogenic and biogenic precursors to show the additional sensitivity information computed by CMAQ-hyd. Compared with the traditional finite difference method, CMAQ-hyd consumes fewer computational resources when the same sensitivity coefficients are calculated. This novel method implemented in CMAQ is also computationally competitive with other existing methods and could be further optimized to reduce memory and computational time overheads.

Spatial distribution of trace elements and potential contamination sources for surface sediments of the North-Western Black Sea, Romania

It is essential to determine the concentrations of metals/metalloids in marine sediments and their contamination status to develop appropriate pollution control strategies and/or improve existing ones. Spatial distribution of aluminum (Al) and some trace elements, i.e., arsenic (As), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), accumulated in surface sediments of the north-western (NW) Black Sea (Romanian zone) was evaluated. Sediment samples were collected in 2019 from 32 stations located at water depths in the range of 12.7–149 m. The mean values ± standard deviations (SD) of element concentrations were as follows: 3.9 ± 1.6% for Al, 11.2 ± 10.2 mg/kg for As, 64.8 ± 27.0 mg/kg for Cr, 32.7 ± 15.0 mg/kg for Cu, 0.10 ± 0.09 mg/kg for Hg, 45.3 ± 23.8 mg/kg for Ni, 24.6 ± 9.6 mg/kg for Pb, and 68.0 ± 28.0 mg/kg for Zn. These values are not significantly different (p &amp;gt; 0.05) from those found in a previous study on surface sediments collected in 2018 from 22 stations located in the same area, but at lower water depths (13.5–67 m). Contamination factor (CF) was used to assess the contamination status of surface sediments. The mean values of CF (0.26–1.23) reveal low to moderate contamination with Pb and low contamination with the other elements. Principal component analysis (PCA) and correlation analysis indicated that As, Cu, Ni, Hg, Pb, and Zn were associated with clay, silt, and organic matter, suggesting that these elements mainly came from the Danube discharges and also from local anthropogenic sources, whereas Al, Cr, and partly Hg, Pb, and Zn originated from rock/soil weathering and erosion. The concentration levels of As, Cu, Hg, Ni, Pb, Zn, TOC, silt, and clay were higher in sediments collected from stations generally located at higher water depths (up to 118 m), suggesting that the TEs associated with the finer carriers were transported offshore by currents and waves, whereas the concentration levels of Al, Cr, Hg, and Zn were higher in sediments with lower levels of CaCO3 content (10.1–24.3%), collected from shallower stations (water depths of 12.7–42.0 m).