Latent Archetypes of the Spatial Patterns of Cancer.

The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2-4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached.

Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation.

Recent advances in spatial transcriptomics (ST) enable measurements of transcriptome within intact biological tissues by preserving spatial information, offering biologists unprecedented opportunities to comprehensively understand tissue micro-environment, where spatial domains are basic units of tissues. Although great efforts are devoted to this issue, they still have many shortcomings, such as ignoring local information and relations of spatial domains, requiring alternatives to solve these problems. Here, a novel algorithm for spatial domain identification in Spatial Transcriptomics data with Structure Correlation and Self-Representation (ST-SCSR), which integrates local information, global information, and similarity of spatial domains. Specifically, ST-SCSR utilzes matrix tri-factorization to simultaneously decompose expression profiles and spatial network of spots, where expressional and spatial features of spots are fused via the shared factor matrix that interpreted as similarity of spatial domains. Furthermore, ST-SCSR learns affinity graph of spots by manipulating expressional and spatial features, where local preservation and sparse constraints are employed, thereby enhancing the quality of graph. The experimental results demonstrate that ST-SCSR not only outperforms state-of-the-art algorithms in terms of accuracy, but also identifies many potential interesting patterns.

Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms.

Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this study, we introduced a new method called iPALM-GLMF, which modelled microbe-disease association prediction as a problem of non-negative matrix factorization with graph dual regularization terms and L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and the L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non-negative matrix factorization and to improve the interpretability. To solve the model, iPALM-GLMF used a non-negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM-GLMF performed better than other existing methods in leave-one-out cross-validation and fivefold cross-validation. In addition, case studies of different diseases demonstrated that iPALM-GLMF could effectively predict potential microbial-disease associations. iPALM-GLMF is publicly available at https://github.com/LiangzheZhang/iPALM-GLMF.

Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China.

Groundwater contaminated by potentially toxic elements has become an increasing global concern for human health. Therefore, it is crucial to identify the sources and health risks of potentially toxic elements, especially in arid areas. Despite the necessity, there is a notable research gap concerning the sources and risks of these elements within multi-layer aquifers in such regions. To address this gap, 54 phreatic and 24 confined groundwater samples were collected from an arid area in Northwest China. This study aimed to trace the sources and evaluate the human health risks of potentially toxic elements by natural background level (NBL), positive matrix factorization (PMF) model, and health risk model. Findings revealed exceeding levels of potentially toxic elements existed in phreatic and confined aquifers. Source apportionment and NBL results indicated that mineral dissolution, evaporation, redox reactions, and human activities were the main factors for elevated concentrations of potentially toxic elements. High Fe and Mn concentrations were attributed to reduction environments, while F accumulation resulted from slow runoff, and irrigation from the Yellow River. Due to high F levels, more than one-third of groundwater samples (phreatic: 33.14 %, confined: 56.22 %) posed non-carcinogenic health risks to population groups. Adults displayed higher carcinogenic risks (phreatic: 19.47 %, confined: 34.16 %) than infants (phreatic: 0 %, confined: 0 %) and children (phreatic: 1.26 %, confined: 7.97 %) owing to the toxic elements of Cr. The confined aquifer presented greater health risks than the phreatic aquifer. Consequently, controlling the levels of F and Cr in multi-layered aquifers is key to reducing health risks. These findings provide valuable insights into protecting groundwater from contamination by potentially toxic elements in multi-layered aquifers worldwide.

Spatial heterogeneity: Necessary and feasible for revealing soil trace elements pollution, sources, risks, and their links.

The source diversity and health risk of trace elements (TEs) in soil make it necessary to reveal the relationship between pollution, source, and risk. However, neglect of spatial heterogeneity restricts the reliability of existing identification methods. In this study, spatial heterogeneity is proposed as a necessary and feasible factor for accurately dissecting the pollution-source-risk link of soil TEs. A comprehensive framework is developed by integrating positive matrix factorization, Geodetector, and risk evaluation tools, and successfully applied in a mining-intensive city in northern China. Overall, the TEs are derived from natural background (28.5 %), atmospheric deposition (25.6 %), coal mining (21.8 %), and metal industry (24.1 %). The formation mechanism of heterogeneity for high-variance TEs (Se, Hg, Cd) is first systematically deciphered by revealing the heterogeneous source-sink relationship. Specifically, Se is dominated (76.5 %) by heterogeneous coal mining (q=0.187), Hg is determined (92.6 %) by the heterogeneity of metal mining (q=0.183) and smelting (q=0.363), and Cd is caused (50.9 %) by heterogeneous atmospheric deposition (q>0.254) co-influenced by the terrains and soil properties. Highly heterogeneous sources are also noteworthy for their potential to pose extreme risks (THI=1.122) in local areas. This study highlights the necessity of integrating spatial heterogeneity in pollution and risk assessment of soil TEs.

Source identification of heavy metals and metalloids in soil using open-source Tellus database and their impact on ecology and human health.

The presence of heavy metals and metalloids (metal(loid)s) in the food chain is a global problem, and thus, metal(loid)s are considered to be Potentially Toxic Elements (PTEs). Arsenic (As), lead (Pb), mercury (Hg), and cadmium (Cd) are identified as prominent hazards related to human health risks throughout the food chain. This study aimed to carry out a source attribution for metal(loid)s in shallow topsoil of north-midlands, northwest, and border counties of the Republic of Ireland, followed by an assessment of the potential ecological and human health risks. The positive Matrix Factorization (PMF) was used for source characterization of PTEs, followed by the Monte Carlo simulation method, used for a probabilistic model to evaluate potential human health risks. The mean concentrations of prioritized metal(loid)s in the topsoil range in the order of Pb (28.83 mg kg-1) > As (7.81 mg kg-1) > Cd (0.51 mg kg-1) > Hg (0.11 mg kg-1) based on the open-source Tellus dataset. This research identified three primary sources of metal(loid) pollution: geogenic sources (36 %), mixed sources of historical mining and natural origin (33 %), and anthropogenic activities (31 %). The ecological risk assessment showed that Ireland's soil exhibits low-moderate pollution levels however, concerns remain for Cd and As levels. All metal(loid)s except Cd showed acceptable non-carcinogenic risk, while Cd and As accounted for high to moderate potential cancer risks. Potato consumption (if grown on land with elevated metal(loid) levels), Cd concentration in soil, and bioaccumulation factor of Cd in potatoes were the three most sensitive parameters. In conclusion, metal(loid)s in Ireland present low to moderate ecological and human health risks. It underscores the need for policies and remedial strategies to monitor metal(loid) levels in agricultural soil regularly and the production of crops with low bioaccumulation in regions with elevated metal(loid) levels.

Long-term contributions of VOC sources and their link to ozone pollution in Bronx, New York City.

Changes in energy and environmental policies along with changes in the energy markets of New York State over the past two decades, have spurred interest in evaluating their impacts on emissions from various energy generation sectors. This study focused on quantifying these effects on VOC (volatile organic compounds) emissions and their subsequent impacts on air quality within the New York City (NYC) metropolitan area. NYC is an EPA nonattainment region for ozone (O3) and likely is a VOC limited region. NYC has a complex coastal topography and meteorology with low-level jets and sea/bay/land breeze circulation associated with heat waves, leading to summertime O3 exceedances and formation of secondary organic aerosol (SOA). To date, no comprehensive source apportionment studies have been done to understand the contributions of local and long-range sources of VOCs in this area. This study applied an improved Positive Matrix Factorization (PMF) methodology designed to incorporate atmospheric dispersion and photochemical reaction losses of VOCs to provide improved apportionment results. Hourly measurements of VOCs were obtained from a Photochemical Assessment Monitoring Station located at an urban site in the Bronx from 2000 to 2021. The study further explores the role of VOC sources in O3 and SOA formation and leverages advanced machine learning tools, XGBoost and SHAP algorithms, to identify synergistic interactions between sources and provided VOC source impacts on ambient O3 concentrations. Isoprene demonstrated a substantial influence in the source contribution of the biogenic factor, emphasizing its role in O3 formation. Notable contributions from anthropogenic emissions, such as fuel evaporation and various industrial processes, along with significant traffic-related sources that likely contribute to SOA formation, underscore the combined impact of natural and human-made sources on O3 pollution. Findings of this study can assist regulatory agencies in developing appropriate policy and management initiatives to control O3 pollution in NYC.

Source Apportionment and Human Health Risks of Potentially Toxic Elements in the Surface Water of Coal Mining Areas.

Contamination with potentially toxic elements (PTEs) frequently occurs in surface water in coal mining areas. This study analyzed 34 surface water samples collected from the Yunnan-Guizhou Plateau for their hydrochemical characteristics, spatial distribution, source apportionment, and human health risks. Our statistical analysis showed that the average concentrations of PTEs in the surface water ranked as follows: Fe > Al > Zn > Mn > Ba > B> Ni > Li > Cd > Mo > Cu > Co > Hg > Se > As > Pb > Sb. The spatial analysis revealed that samples with high concentrations of Fe, Al, and Mn were predominantly distributed in the main stream, Xichong River, and Yangchang River. Positive matrix factorization (PMF) identified four sources of PTEs in the surface water. Hg, As, and Se originated from wastewater discharged by coal preparation plants and coal mines. Mo, Li, and B originated from the dissolution of clay minerals in coal seams. Elevated concentrations of Cu, Fe, Al, Mn, Co, and Ni were attributed to the dissolution of kaolinite, illite, chalcopyrite, pyrite, and minerals associated with Co and Ni in coal seams. Cd, Zn, and Pb were derived from coal melting and traffic release. The deterministic health risks assessment showed that 94.12% of the surface water samples presented non-carcinogenic risks below the health limit of 1. Meanwhile, 73.56% of the surface water samples with elevated As posed level III carcinogenic risk to the local populations. Special attention to drinking water safety for children is warranted due to their lower metabolic capacity for detoxifying PTEs. This study provides insight for PTE management in sustainable water environments.

[Pollution Characteristics and Source Apportionment of Heavy Metals in Topsoil of Counties Along the Shandong Section of the Yellow River].

The 25 counties along the Shandong section of the Yellow River are the core areas for promoting the ecological protection and high-quality development of the Yellow River in Shandong Province. Moreover, it is of great significance to study the current situation, sources, and potential risks of heavy metal pollution in the topsoil in this region. In this study, 103 soil samples were collected from the 25 counties along the Shandong section of the Yellow River, and the contents of eight heavy metals （As, Cu, Pb, Cr, Zn, Ni, Cd, and Hg） were determined. The pollution characteristics of heavy metals were analyzed and evaluated using the geological accumulation index and potential ecological risk index. Correlation analysis and the positive matrix factorization （PMF） model were used to analyze the sources of heavy metals. The results showed that the average contents of Cu and Cr were lower than that of the background values of soils, whereas the average contents of As, Pb, Zn, Ni, Cd, and Hg were 1.16, 1.42, 1.05, 1.14, 2.29, and 1.85 times higher than that of the background values, respectively, and the average contents of all eight elements were lower than the screening value of soil pollution risk in agricultural land. In terms of different heavy metal variations, the coefficient of variation （CV） of Cu and Cd was higher than 0.500, indicating high variations, whereas As, Pb, Cr, Zn, Ni, and Hg showed moderate variation. Cd and Hg were slightly polluted, whereas the other six elements were not polluted. Cd and Hg had a moderate potential ecological risk level, whereas the other six elements were at a low level. Correlation analysis and PMF model showed that the sources of heavy metals in the study area were influenced by four factors, i.e., agricultural activities, natural sources, industrial emissions, and atmospheric dust from coal combustion and vehicle exhaust emissions, and the relative contribution rates were 32.4%, 34.9%, 16.5%, and 16.2%, respectively.

CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.

Predicting associations between microbes and diseases opens up new avenues for developing diagnostic, preventive, and therapeutic strategies. Given that laboratory-based biological tests to verify these associations are often time-consuming and expensive, there is a critical need for innovative computational frameworks to predict new microbe-disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction profile similarity of microbes. We then apply the Weighted K Nearest Known Neighbors (WKNKN) algorithm to refine the microbe-disease association matrix. Our CMFHMDA model is subsequently developed by integrating the network data of both microbes and diseases to predict potential associations. The key innovations of this method include using the WKNKN algorithm to preprocess missing values in the association matrix and incorporating cross-domain information from microbes and diseases into the CMFHMDA model. To validate CMFHMDA, we employed three different cross-validation techniques to evaluate the model's accuracy. The results indicate that the CMFHMDA model achieved Area Under the Receiver Operating Characteristic Curve scores of 0.9172, 0.8551, and 0.9351$\pm $0.0052 in global Leave-One-Out Cross-Validation (LOOCV), local LOOCV, and five-fold CV, respectively. Furthermore, many predicted associations have been confirmed by published experimental studies, establishing CMFHMDA as an effective tool for predicting potential disease-associated microbes.

Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based approach.

The unique non-coding RNA molecule known as circular RNA (circRNA) is distinguished from conventional linear RNA by having a longer half-life, greater degree of conservation and inherent solidity. Extensive research has demonstrated the profound impact of circRNA expression on cellular drug sensitivity and therapeutic efficacy. There is an immediate need for the creation of efficient computational techniques to anticipate the potential correlations between circRNA and drug sensitivity, as classical biological research approaches are time-consuming and costly. In this work, we introduce a novel deep learning model called SNMGCDA, which aims to forecast the relationships between circRNA and drug sensitivity. SNMGCDA incorporates a diverse range of similarity networks, enabling the derivation of feature vectors for circRNAs and drugs using three distinct calculation methods. First, we utilize a sparse autoencoder for the extraction of drug characteristics. Subsequently, the application of non-negative matrix factorization (NMF) enables the identification of relationships between circRNAs and drugs based on their shared features. Additionally, the multi-head graph attention network is employed to capture the characteristics of circRNAs. After acquiring the characteristics from these three separate components, we combine them to form a unified and inclusive feature vector for each cluster of circRNA and drug. Finally, the relevant feature vectors and labels are inputted into a multilayer perceptron (MLP) to make predictions. The outcomes of the experiment, obtained through 5-fold cross-validation (5-fold CV) and 10-fold cross-validation (10-fold CV), demonstrate SNMGCDA outperforms five other state-of-art methods in terms of performance. Additionally, the majority of case studies have predominantly confirmed newly discovered correlations by SNMGCDA, thereby emphasizing its reliability in predicting potential relationships between circRNAs and drugs.

Improved Non-Negative Matrix Factorization-Based Noise Reduction of Leakage Acoustic Signals.

The detection of gas leaks using acoustic signals is often compromised by environmental noise, which significantly impacts the accuracy of subsequent leak identification. Current noise reduction algorithms based on non-negative matrix factorization (NMF) typically utilize the Euclidean distance as their objective function, which can exacerbate noise anomalies. Moreover, these algorithms predominantly rely on simple techniques like Wiener filtering to estimate the amplitude spectrum of pure signals. This approach, however, falls short in accurately estimating the amplitude spectrum of non-stationary signals. Consequently, this paper proposes an improved non-negative matrix factorization (INMF) noise reduction algorithm that enhances the traditional NMF by refining both the objective function and the amplitude spectrum estimation process for reconstructed signals. The improved algorithm replaces the conventional Euclidean distance with the Kullback-Leibler (KL) divergence and incorporates noise and sparse constraint terms into the objective function to mitigate the adverse effects of signal amplification. Unlike traditional methods such as Wiener filtering, the proposed algorithm employs an adaptive Minimum Mean-Square Error-Log Spectral Amplitude (MMSE-LSA) method to estimate the amplitude spectrum of non-stationary signals adaptively across varying signal-to-noise ratios. Comparative experiments demonstrate that the INMF algorithm significantly outperforms existing methods in denoising leakage acoustic signals.

Machine Learning Techniques for Blind Beam Alignment in mmWave Massive MIMO.

This paper proposes methods for Machine Learning (ML)-based Beam Alignment (BA), using low-complexity ML models, and achieves a small pilot overhead. We assume a single-user massive mmWave MIMO, Uplink, using a fully analog architecture. Assuming large-dimension codebooks of possible beam patterns at UE and BS, this data-driven and model-based approach aims to partially and blindly sound a small subset of beams from these codebooks. The proposed BA is blind (no CSI), based on Received Signal Energies (RSEs), and circumvents the need for exhaustively sounding all possible beams. A sub-sampled subset of beams is then used to train several ML models such as low-rank Matrix Factorization (MF), non-negative MF (NMF), and shallow Multi-Layer Perceptron (MLP). We provide an extensive mathematical description of these models and the algorithms for each of them. Our extensive numerical results show that, by sounding only 10% of the beams from the UE and BS codebooks, the proposed ML tools are able to accurately predict the non-sounded beams through multiple transmitted power regimes. This observation holds as the codebook sizes at UE and BS vary from 128×128 to 1024×1024.

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.

Arsenic-contaminated groundwater of the Western Banat (Pannonian basin): Hydrogeochemical appraisal, pollution source apportionment, and Monte Carlo simulation of source-specific health risks.

Due to rapid urbanization and industrial growth, groundwater globally is continuously deteriorating, posing significant health risks to humans. This study employed a comprehensive methodology to analyze groundwater in the Western Banat Plain (Serbia). Using Piper and Gibbs plots, hydrogeochemistry was assessed, while the entropy-weighted water quality index (EWQI) was used to evaluate groundwater quality. Pollution sources were identified using positive matrix factorization (PMF) accompanied by Pearson correlation and hierarchical cluster analysis, while Monte Carlo simulation assessed health risks associated with groundwater consumption. Results showed that groundwater, mainly Ca-Mg-HCO3 type, is mostly suitable for drinking. Geogenic pollution, agricultural activities, and sewage were major pollution sources. Consumption of contaminated groundwater poses serious non-carcinogenic and carcinogenic health risks. Additionally, arsenic from geogenic source was found to be the main health risks contributor, considering its worryingly elevated concentration, ranging up to 364 µg/L. These findings will be valuable for decision-makers and researchers in managing groundwater vulnerability. PRACTITIONER POINTS: Groundwater is severely contaminated with As in the northern part of the study area. The predominant hydrochemical type of groundwater in the area is Ca-Mg-HCO3. The PMF method apportioned three groundwater pollution sources. Monte Carlo identified rock dissolution as the primary health risk contributor. Health risks and mortality in the study area are positively correlated.

Integrating multi-omics data of childhood asthma using a deep association model.

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases.

[Characteristics and Source Analysis of VOCs Pollution During Emergency Response in Tianjin].

To elucidate the characteristics of VOCs chemical components during heavy pollution episodes, hourly online VOCs data derived from 11 heavy pollution events in Tianjin from 2019 to 2020 were employed. The positive matrix factorization （PMF） and conditional bivariate probability function （CBPF） were employed to analyze the sources of VOCs during heavy pollution episodes. The results indicated that the average VOCs volume fraction during these episodes was recorded at 35.7×10-9. Furthermore, it was observed that during the winter emergency response period, there was a discernible increase in the volume fraction of VOCs when compared to that during the autumn season. Specifically, there was a notable upswing of 48% in the olefins category, whereas alkanes registered a 4% increase. Additionally, the VOCs component structure changed significantly during the heavy pollution episodes. During the orange warning period, the proportion of alkanes increased by 36%, and the proportion of acetylene decreased by 32%. During the yellow warning period, the proportion of alkanes increased by 14%, and the proportion of acetylene decreased by 5%. During the emergency response period, motor vehicle emission sources, natural gas evaporative sources, and solvent use sources were the main contributors of VOCs in environmental receptors, contributing 17.5%, 15.4%, and 15.2%, respectively. Compared with that during the period antecedent to the emergency response, the contribution of vehicle emission sources and diesel volatile sources to VOCs in environmental receptors decreased by 2.0% to 5.5% and 2.1% to 6.6%, respectively, and the contribution of solvent use sources decreased by 0.2% to 2.4% during the yellow warning period. During the orange warning period, the contribution of motor vehicle emission sources was reduced by 0.1% to 8.3%, and the contribution of solvent use sources was reduced by 0.5% to 6.2%.

[Sensitivity Analysis of Atmospheric Ozone and Source Apportionment of Volatile Organic Compounds in Yinchuan City].

Based on the observation data of O3 concentration in Yinchuan in 2022, the monthly variation characteristics of O3 concentrations were analyzed. Further, based on the observation data of meteorological elements, conventional pollutants, and volatile organic compounds （VOCs） concentrations at an urban site in Yinchuan from May to July, the difference in meteorological elements and precursor concentrations between the polluted days and the non-polluted days were compared. Then, the O3 sensitivity and the VOCs sources were discussed using the Framework for 0-D Atmospheric Modeling （F0AM） and positive matrix factorization （PMF） model, respectively. The results showed that： ① The O3 pollution occurred from May to July in 2022, and the concentrations of O3-8h-90per were 156 µg·m-3, 170 µg·m-3, and 174 µg·m-3, respectively, with exceeding standard rates of 9.7%, 26.7%, and 29.0%, respectively. ② Compared with those on the non-polluted days, the hourly mean values of temperature, total solar radiation, and concentrations of various precursors on the O3-polluted days increased, including the volume concentrations of propane, isobutane, ethane, n-butane, and dichloromethane, which increased significantly by 33.1%, 29.1%, 25.0%, 22.7%, and 21.3%, respectively. The results showed that the combined increase in pollutant emissions and adverse meteorological conditions contributed to the formation of O3. ③ From May to July 2022, the top five VOCs species in terms of ozone formation potential （OFP） value on whole, non-polluted, and polluted days were the same. They were acetaldehyde, m/p-xylene, ethylene, isoprene, and toluene, mainly from solvent use sources, natural sources, and chemical industry emissions. ④ The local O3 production was mostly controlled by VOCs, and the relative incremental reactivity （RIR） results revealed that O3 production showed strong positive sensitivity to alkene and aromatic hydrocarbon but showed negative sensitivity to NOx on both polluted and non-polluted days. The relative contributions of active species such as acetone, ethylene, and isobutane to O3 production were high, and the implementation of an emission reduction scheme with the ratio of VOCs to NOx emission reduction much greater than 1 could effectively reduce the local O3 concentration. ⑤ The main sources of atmospheric VOCs in Yinchuan were motor vehicle emission sources （32.3%）, process sources （20.7%）, combustion sources （19.2%）, solvent use sources （12.7%）, gasoline volatile sources （9.1%）, and natural sources （6%）, and the contribution rate of motor vehicle emission sources on polluted days increased by 4.6% compared with that on non-polluted days, indicating that the motor vehicle emission source was an important object of summer VOCs control in Yinchuan.

[Analysis of Heavy Metal Sources in Farmland Soil of Sewage Irrigation and Industrial Complex Area Based on APCS-MLR and PMF].

The contents of eight heavy metals （Cr, Ni, Cu, Zn, Cd, Pb, As, and Hg） were determined based on the surface soil samples of sewage irrigation and industrial complex in Kaifeng City. The absolute factor analysis-multiple linear regression （APCS-MLR） model and positive matrix factorization （PMF） model were used to analyze the sources and contribution rates of heavy metals in soil combined with correlation analysis and systematic cluster analysis. The results showed that： ① The average values of ω（Cr）, ω（Ni）, ω（Cu）, ω（Zn）, ω（Cd）, ω（Pb）, ω（As）, and ω（Hg） in the study area were 52.19, 25.00, 42.03, 323.53, 1.79, 53.45, 9.43, and 0.20 mg·kg-1, respectively, and Cr, Ni, and As are lower than the background values of tidal soil. Cu, Zn, Cd, Pb, and Hg are higher than the background values of the tidal soil. ② There were four sources of the eight heavy metals： natural sources, agricultural sewage irrigation sources, industrial atmospheric sedimentation sources, and transportation sources. Cr and Ni were mainly from natural sources； Cu, Zn, Cd, and Pb were mainly from agricultural sewage irrigation and transportation sources； As was mainly from natural sources and agricultural sewage irrigation； and Hg was mainly from industrial atmospheric sedimentation. ③ The APCS-MLR and PMF source analysis results indicated that industrial and agricultural activities were the main sources of heavy metals in the soil of the study area. The average contribution rates of APCS-MLR in the nine sampling areas of the research area were 76.01% （natural sources and agricultural sewage irrigation sources）, 22.71% （industrial atmospheric sedimentation sources and transportation sources）, and 1.28% （unknown sources）. The average contribution rates of PMF were 59.66% （natural sources and agricultural sewage irrigation sources） and 40.34% （industrial atmospheric sedimentation sources and transportation sources）. The source analysis results of the LZ, XZ, NLT, PT, YLZ, and BC models were basically consistent, and WL was better in the APCS-MLR model, whereas SG and QT were better in the PMF model. The research results can provide a scientific basis for the prevention and control of soil heavy metal pollution and environmental remediation.