Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM2.5) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53-1.74 and 0.44-1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future.

Primary and oxidative source analyses of consumed VOCs in the atmosphere.

Consumed VOCs are the compounds that have reacted to form ozone and secondary organic aerosol (SOA) in the atmosphere. An approach that can apportion the contributions of primary sources and reactions to the consumed VOCs was developed in this study and applied to hourly VOCs data from June to August 2022 measured in Shijiazhuang, China. The results showed that petrochemical industries (36.9 % and 51.7 %) and oxidation formation (20.6 % and 35.6 %) provided the largest contributions to consumed VOCs and OVOCs during the study period, whereas natural gas (5.0 % and 7.6 %) and the mixed source of liquefied petroleum gas and solvent use (3.1 % and 4.2 %) had the relatively low contributions. Compared to the non-O3 pollution (NOP) period, the contributions of oxidation formation, petrochemical industries, and the mixed source of gas evaporation and vehicle emissions to the consumed VOCs during the O3 pollution (OP) period increased by 2.8, 3.8, and 9.3 times, respectively. The differences in contributions of liquified petroleum gas and solvent use, natural gas, and combustion sources to consumed VOCs between OP and NOP periods were relatively small. Transport of petrochemical industries emissions from the southeast to the study site was the primary consumed pathway for VOCs emitted from petrochemical industries.

[Recent findings and advances in treatment of myeloproliferative neoplasms].

Many novel agents have been developed for BCR::ABL1-negaive myeloproliferative neoplasms (MPN), namely, polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). Some of these agents not only achieve hematologic complete response, reduce spleen size, and alleviate constitutional symptoms, but also induce molecular response, which means that they reduce the allele burden of driver gene mutations. These agents also prevent and alleviate fibrosis in bone marrow, which reduces the incidence of thrombotic events and disease progression and might improve prognosis. This article discusses the latest findings and promising treatments, including ongoing clinical trials, in PV, ET, and PMF.

[Characterization of Metal Elements in Atmospheric Fine Particulate Matter and Their Sources in Winter in the Southern Sichuan Urban Agglomeration].

To investigate the concentration characteristics and sources of metal elements in PM2.5 during winter heavy pollution in the southern Sichuan urban agglomeration (Zigong, Luzhou, Neijiang, and Yibin), the metal elements in PM2.5 were measured using membrane sampling methods from December 30, 2018 to January 14, 2019, and the enrichment factor method (EF) and positive matrix factorization(PMF) were applied to investigate the sources of metal elements. The metal element observation data of Zigong in the same period of 2015 were also used to investigate the changes in metal element pollution and enrichment in Zigong in the middle and end of the implementation of China's Air Pollution Prevention and Control Action Plan. The main findings were as follows:① The concentrations and percentages of metal elements in particulate matter in different cities did not differ significantly. The elements with higher concentrations in the four cities showed similarities, with Al, Sb, and Fe at the top. From the comparison of different observation periods in Zigong, the concentrations of all elements except Tl changed. ② The results of the enrichment factor calculation showed that the enrichment of the elements Cr (Zigong and Yibin), Ni, Cu, As, Se, Ag, Cd, Sb, Tl, and Pb in the urban agglomeration was high. The comparison of the enrichment levels of elements in Zigong for different observation periods showed that the enrichment levels of all elements, except Cu, tended to decrease in the winter observation period of 2018. ③ The results of PMF source analysis showed that the metal elements in each city mainly originated from dust sources, coal-fired sources, industrial sources, and traffic sources, whereas there was a mixed contribution among the sources. The contribution of the main sources differed among cities, in which Zigong was dominated by traffic dust sources and mixed sources, Luzhou was dominated by industrial sources, Neijiang had a similar contribution from different sources, and Yibin was dominated by traffic sources.

Air quality challenges in Central Asian urban areas: a PM2.5 source apportionment analysis in Dushanbe, Tajikistan.

This work presents the first comprehensive assessment of PM pollution sources in Dushanbe, Tajikistan. A total of 138 PM2.5 samples were collected during 2015-2016 and 2018-2019 and were analyzed through gravimetric, ED-XRF, and multi-wavelength absorption techniques. The results show that PM2.5 concentrations were substantially higher than the European annual limit value and WHO Air Quality Guidelines annual average value, with an average of 90.9 ± 68.5 µg m-3. The PMF application identified eight sources of pollution that influenced PM2.5 concentration levels in the area. Coal burning (21.3%) and biomass burning (22.3%) were the dominant sources during the winter, while vehicular traffic (7.7%) contributed more during the warm season. Power plant emissions (17.5%) showed enhanced contributions during the warm months, likely due to high energy demand. Cement industry emissions (6.9%) exhibited significant contribution during the cold period of 2018-2019, while soil dust (11.3%) and secondary sulphates (11.5%) displayed increased contribution during the warm and cold months, respectively. Finally, waste burning (1.5%) displayed the lowest contribution, with no significant temporal variation. Our results highlight the significant impact of anthropogenic activities, and especially the use of coal burning for energy production (both in power plants and for residential heating), and the significant contribution of biomass burning during both warm and cold seasons.

Annual variation of source contributions to PM10 and oxidative potential in a mountainous area with traffic, biomass burning, cement-plant and biogenic influences.

Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM10 sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM10 filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM10 were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM10, OPAA and OPDDT, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM10 mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM10 and OP in complex environments and are thus useful for PM control strategies and actions.

Source specific sedimentary response towards the differential anthropogenic impacts in terms of potentially toxic elements in an urban river.

To investigate the interplay between varying anthropogenic activities and sediment dynamics in an urban river (Turag, Bangladesh), this study involved 37-sediment samples from 11 different sections of the river. Neutron activation analysis and atomic absorption spectrometry were utilized to quantify the concentrations of 14 metal(oid)s (Al, Ti, Co, Fe, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn). This study revealed significant toxic metal trends, with Principal coordinate analysis explaining 62.91 % of the variance from upstream to downstream. The largest RSDs for Zn(287 %), Mn(120 %), and Cd(323 %) implies an irregular regional distribution throughout the river. The UNMIX-model and PMF-model were utilized to identify potential sources of metal(oid)s in sediments. ∼63.65-66.7 % of metal(oid)s in sediments originated from anthropogenic sources, while remaining attributed to natural sources in both models. Strikingly, all measured metal(oid)s' concentrations surpassed the threshold effect level, with Zn and Ni exceeding probable effect levels when compared to SQGs.

Distinct anthropogenic signatures: A comparative analysis of polycyclic aromatic hydrocarbons in sediments from two southeastern Chinese bays.

Sansha and Luoyuan Bay are influenced by different industrial structure, but the sources and pollution status of polycyclic aromatic hydrocarbons (PAHs), especially alkylated PAHs, are poorly understood. We studied 25 PAHs in surface sediments from the two bays. The results showed that PAHs concentrations in Sansha and Luoyuan Bay sediment range from 6.54 to 479.28 ng/g and 118.82 to 2984.09 ng/g, respectively. Alkylated PAHs dominated in Sansha (48.86 % of Σ25PAHs), while 3-ring PAHs dominated in Luoyuan (36.32 % of ∑25PAHs). Results of sources analysis indicated oil spills as the main PAHs source in Sansha, and domestic emissions and fossil fuel combustion in Luoyuan. Ecological risk assessment of showed low sediment risk, but in Luoyuan was higher than in Sansha. Compared with Luoyuan Bay, Sansha Bay emits less industrial pollutants, so the pollution is lower than Luoyuan Bay. Increased attention to protecting Luoyuan Bay is recommended.

Impact of terephthalic acid emissions from intensive nocturnal biomass incineration on oxidative potential in Seoul, South Korea.

This study investigated the impact of large-scale incineration facilities on PM2.5 levels in Seoul during winter. Due to the challenge of obtaining accurate combustion data from external sources, heat supply records were used as a proxy for combustion activity. To assess health risks, dithiothreitol-oxidative potential (DTT-OP) was analyzed to identify potential hazards to human health. By comparing DTT-OP with PM2.5 sources related to combustion, the study aimed to understand the impact of local pollution sources on human health in Seoul. The diurnal analysis showed that oxidative potential (0.19 µM/m3) and the biomass burning factor (5.53 µg/m3) peaked between 4:00 and 8:00 AM, with lower levels observed from 12:00 to 20:00. A significant correlation was found between combustion sources and oxidative potential, with a high correlation coefficient (r2 = 0.92). The presence of terephthalic acid (TPA) in the Cellulose combustion source profile, which is produced by the pyrolysis of plastics like polyester fiber and polyethylene terephthalate (PET), further supported the link to emissions from incineration facilities. These findings suggest that the biomass burning source is strongly correlated with DTT-OP, indicating a significant association with health risks among various local sources of PM2.5 in Seoul.

Quantitative tracing the sources and human risk assessment of complex soil pollution in an industrial park.

Pollution in industrial parks has long been characterized by complex pollution sources and difficulties in identifying pollutant origins. This study focuses on a typical industrial park consisting of 11 factories (F1-F11) including organic pigment, inorganic pigment, and chemical factories in Hunan Province, China, here, a total of 327 sample points were surveyed. Eight pollutants (Mn, Cd, As, Co, NH3-N, l, 1,2-Trichloroethane, chlorobenzene, and petroleum hydrocarbons) were classified as contaminants of concern (COCs). This study assessed the contributions of driving factors to the distribution of COCs in the soil. Pollutant source apportionment was conducted using positive matrix factorization (PMF) and random forest (RF). The results revealed that the main factors driving pollution are groundwater migration, non-compliant emissions, leaks during production, and interactions among pollutants. The primary pollution sources were four chemical factories and an inorganic pigment factory. Source 5 demonstrates significant correlations with TCA (29.6%), CB (30%), and As (31.6%). Two chemical factories (F7 and F10) are the most significant pollution source with a risk assessment contribution rate of more than 60%. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of COCs in industrial park. By utilizing the proposed research framework, decision-makers can effectively prioritize and address identified pollution sources.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies.

Antimicrobial peptides (AMPs) are increasingly recognized as potent therapeutic agents, with their selective affinity for pathological membranes, low toxicity profile, and minimal resistance development making them particularly attractive in the pharmaceutical landscape. This study offers a comprehensive analysis of the interaction between specific AMPs, including magainin-2, pleurocidin, CM15, LL37, and clavanin, with lipid bilayer models of very different compositions that have been ordinarily used as biological membrane models of healthy mammal, cancerous, and bacterial cells. Employing unbiased molecular dynamics simulations and metadynamics techniques, we have deciphered the intricate mechanisms by which these peptides recognize pathogenic and pathologic lipid patterns and integrate into lipid assemblies. Our findings reveal that the transverse component of the peptide's hydrophobic dipole moment is critical for membrane interaction, decisively influencing the molecule's orientation and expected therapeutic efficacy. Our approach also provides insight on the kinetic and dynamic dependence on the peptide orientation in the axial and azimuthal angles when coming close to the membrane. The aim is to establish a robust framework for the rational design of peptide-based, membrane-targeted therapies, as well as effective quantitative descriptors that can facilitate the automated design of novel AMPs for these therapies using machine learning methods.

Source identification and driving factor apportionment for soil potentially toxic elements via combining APCS-MLR, UNMIX, PMF and GDM.

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil.

Characteristics, sources, and health risks of volatile organic compounds in different functional regions of Shenyang.

The concentration of 56 volatile organic compounds (VOCs) in the ambient air of Shenyang was continuously monitored at four sites in 2021. The characteristics, sources, secondary pollution potential and health risks of VOCs in different functional regions of Shenyang were discussed. The results indicate that the concentration of VOCs in industrial regions was significantly higher than that in non-industrial regions, with a mean of 41.09 ± 69.82 parts per billion volumes (ppbv) compared to 19.99 ± 17.86 ppbv (commercial & residential region in urban fringe), 27.51 ± 28.81 ppbv (educational & scenic region) and 29.71 ± 23.97 ppbv (commercial & residential region in urban center). The positive matrix factorization (PMF) model was utilized to assign the sources of VOCs in Shenyang, and six factors were recognized: gasoline vehicles (34.8 %), diesel vehicles (28.3 %), combustion (11.4 %), biogenic emissions (9.7 %), industrial processes (8.2 %), and fuel evaporation (7.7 %). The results of the reactivity evaluation indicated that the ozone (O3) formation potential (OFP) was primarily influenced by industrial processes (29.2 %), diesel vehicles (25.7 %), biogenic emissions (17.0 %). These three factors were also the top three contributors to secondary organic aerosol formation potential (SOAP), accounting for 44.2 %, 9.4 % and 30.3 %, respectively. At the all four sites, the non-carcinogenic and carcinogenic risks of VOCs ranged from 1.6 × 10-2 to 3.8 × 10-2 and from 2.3 × 10-6 to 3.3 × 10-6, respectively. And the main risks can be attributed to emissions from industrial processes and gasoline vehicles. These findings suggested to strengthen the control of vehicle emissions throughout all regions in Shenyang and industrial processes emissions in industrial regions.

[Health Risk Assessment of Heavy Metals in Soils of a City in Guangdong Province Based on Source Oriented and Monte Carlo Models].

Taking a city in Guangdong Province as the research area, the concentration and spatial distribution characteristics of heavy metals in the surface soil were studied to clarify the situation of soil heavy metal pollution and priority control factors, providing basic data for the prevention and control of soil heavy metal pollution in the city. The content characteristics of heavy metals in 221 soil samples in the city were analyzed, and the potential health risk assessment and source analysis were carried out through the Monte Carlo model, the potential health risk assessment (HRA) model, and the PMF receptor model. It was found that heavy metals ω(As), ω(Hg), ω(Cd), ω(Pb), ω(Cr), ω(Cu), ω(Ni), and ω(Zn) in the soil of the city were 18.16, 0.43, 1.46, 68.57, 98.34, 64.19, 26.53, and 257.32 mg·kg-1, respectively, with a moderate to high degree of variation. Except for Ni concentration, the soil concentrations of other heavy metal elements exceeded the background values of soil in Guangdong Province to a certain extent, and the concentrations of Cd and Zn exceeded the national secondary standards, resulting in severe heavy metal pollution; the main sources of heavy metals were industrial sources, and natural parent materials, lead battery manufacturing, transportation, artificial cultivation, and pesticide and fertilizer inputs also had an undeniable impact on the accumulation of heavy metals in the soil. Heavy metals in the soil had a certain degree of tolerable carcinogenic health risk for both children and adults, whereas non-carcinogenic risks could be ignored. The potential health risk of children was greater than that of adults, and the main exposure route was through oral intake. The input sources of pesticides and fertilizers and As should be the main controlling factors for the health risks of heavy metals in the city's soil, followed by mixed sources and Cr. There were differences in the spatial distribution characteristics and relative pollution levels of heavy metals, and it is necessary to deepen zoning monitoring and control, strengthen soil pollution prevention and control, and reduce human input of heavy metals in soil.

Pollution sources apportionment and suitability assessment of Lah River, Ethiopia: Conjunctive application of multivariate statistical analysis and water quality index.

This study aimed to assess spatiotemporal water quality variation and its suitability for irrigation and domestic purposes in Lah River using the irrigation water quality index (IWQI) and the weighted arithmetic water quality index (WAWQI). The IWQI analysis result showed that the sodium absorption ratio, residual sodium carbonate, potential salinity, Kelly index, magnesium ratio, sodium percentage, and permeability index were found to be 1.07 mEq/L, -0.43 mEq/L, 0.8 mEq/L, 0.78 mEq/L, 43.01%, 42.95%, and 63.46%, respectively. The IWQIs revealed that the water quality of the river was appropriate for agricultural use during the dry season. Furthermore, the calculated WAWQI of the river water ranged from 123.13 to 394.72 during the wet season, indicating the high pollution levels in the Lah River and incompatibility for drinking purposes. On the other hand, the principal component analysis identified two pollution sources during the wet season and three during the dry season. In addition, the positive matrix prioritization model predicted the pollution source's contribution quite well with a signal-to-noise ratio of >2 and a residual error between -3 and 3 for both seasons. This study suggests that water quality of Lah River is degrading periodically necessitating proper pollution management.

Source apportionment of polychlorinated biphenyls in the sediment of the Newtown Creek superfund site.

Polychlorinated biphenyls (PCBs) are a primary contaminant of potential concern at the Newtown Creek superfund site. Measurements of PCBs in hundreds of samples of sediment (surface and cores) within Newtown Creek and at nearby reference locations were obtained from the Remedial Investigation (RI) databases. This data set was analyzed using Positive Matrix Factorization (PMF). A weight-of-evidence approach was used to attribute the PMF-generated fingerprints to sources. The PMF analysis generated eight factors (fingerprints or sources) that represent primary sources, such as Aroclors, as well as secondary sources, including the East River and Combined Sewer Outfalls (CSOs). In addition to the high-production volume Aroclors (1016/1242, 1248, 1254, and 1260), some less-widely used Aroclors (1232 and 1268) were found in Newtown Creek sediment. Aroclor 1268 is disproportionately abundant in the deepest sediments, while PCBs likely from CSOs are relatively more abundant in surface sediment.

Identification of groundwater pollution sources and health risk assessment in the Fengshui mining area of Central Shandong, China.

The crux of groundwater protection lies in a profound understanding of the sources of pollutants and their impacts on human health. This study selected 47 groundwater samples from the Fengshui mining area in central Shandong Province, China, employing advanced hydrogeochemical techniques, positive matrix factorization (PMF), and Monte Carlo analysis methods, aimed at unveiling the characteristics, origins, and health risks of water pollutants. The results indicated that the majority of samples exhibited a slightly alkaline nature. Notably, the concentrations of fluoride (F-) and nitrate (NO3-) exceeded China's safety standards in 40.43% and 23.40% of the samples, respectively. Moreover, a water quality index (WQI) below 50 was observed in approximately 68.09% of the sites, suggesting that the water quality in these areas generally met acceptable levels. However, regions with higher WQI values were predominantly located in the northern and southern parts of the mining area. PMF analysis revealed that regional geological and industrial activities were the primary factors affecting water quality, followed by mining discharges, fundamental geological and agricultural processes, and leachate enrichment activities. The health risk assessment highlighted the heightened sensitivity of the youth demographic to fluoride, with a more pronounced non-carcinogenic risk compared to nitrate, affecting about 31.89% of the youth population. Hence, it is imperative for local authorities and relevant departments to take prompt actions to remediate groundwater contamination to minimize public health risks.

[Characteristics and Identification Priority Source of Heavy Metals Pollution in Farmland Soils in the Yellow River Basin].

Assessments of the soil environmental quality of farmland and pollution source apportionment are the foundation for ensuring national food security and agricultural sustainable development, as well as an important prerequisite for the pursuit to keep our lands clean. This study evaluated the characteristics of heavy metal pollution in farmland soils in the Yellow River Basin from 2000 to 2023, based on the data of heavy metal contents including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, using the geo-accumulation index method. Source apportionment was conducted by employing a positive matrix factorization (PMF) model. The probabilistic health risks were evaluated by coupling Monte Carlo simulation with a human health risk assessment model, and priority pollution sources and elements were identified. The results showed that:① the average content of all heavy metals in farmland soils within the study area was lower than the screening values specified in the soil environment quality risk control standard for soil contamination of agriculture land (GB 15618-2018) (pH>7.5). However, the contents of Cd, As, and Zn in the samples exceeded their screening values, with percentages of 21.69%, 5.56%, and 1.23%, respectively, with Cd having the highest rate of exceedance. ② Hg and Cd were moderately polluted, Cu and Pb were slightly polluted, and the other elements were not polluted. ③ The main sources of heavy metals in farmland soil were traffic-industrial sources, natural-agricultural sources, industrial-natural sources, and agricultural-industrial sources, with contribution rates of 37.04%, 26.69%, 21.72%, and 14.55%, respectively. ④ Heavy metals in farmland soil posed carcinogenic health risks to adults and children but did not have non-carcinogenic risks; As and Cd were priority control elements for human health risks, and industrial-natural sources and agricultural-industrial sources were priority control sources in the study area.

[Distribution Characteristics, Source Analysis and Ecological Risk Assessment of Heavy Metals in the Typical Industry Reclaimed Soil].

To further explore the characteristics of heavy metal pollution and the ecological risk of typical industries in reclaimed soil, based on data from 315 different depth profiles of soil samples collected from 49 plots in Jiading District, Shanghai, the geo-accumulation index and potential ecological risk index were used to evaluate the contents and potential ecological risk of seven heavy metals, namely Cd, Pb, Cu, Zn, Ni, Hg, and As. The APCS-MLR receptor model and PMF positive matrix factorization model were employed to analyze the pollution sources. The results showed that:① except for As, the contents of other heavy metals in the soil of the study area exceeded the Shanghai soil background values to varying degrees. The contents of Cd, Pb, Cu, Zn, Ni, and Hg in the surface soil were 3.54, 2.34, 2.91, 1.20, 3.75, and 4.40 times the background values, respectively. The contents of heavy metals in the soil decreased with the increase in depth, and heavy metals were enriched to a certain extent in the surface soil, indicating that human activities had an impact on the distribution of heavy metals in the soil. ② The APCS-MLR and PMF receptor models identified four main sources of soil heavy metals in the study area. Source 1 (Cu, Zn, and Pb) was a mixture of metal products and automobile manufacturing, source 2 (Ni and Cd) was electroplating enterprises, source 3 (Hg) was mainly from chemical enterprises, and source 4 (As) was natural. The combined use of the two receptor models further improved the accuracy and credibility of source identification. ③ The geo-accumulation index in descending order was Hg(1.54)>Ni(1.32)>Cd(1.21)>Cu(0.96)>Pb(0.64)>Zn(-0.33)>As(-1.02). The potential ecological risk index showed that the comprehensive potential ecological risk index RI value in the study area ranged from 32.50 to 4 910.97, with a mean of 321.40, indicating a strong potential ecological risk. The pollution values of heavy metals Hg, Ni, and Cd in industrial site soil deserve further attention for re-development and utilization purposes.