Quantitative Source Apportionment of Potentially Toxic Elements in Baoshan Soils Employing Combined Receptor Models

Arable soils are crucial for national development and food security; therefore, contamination of agricultural soils from potentially toxic elements (PTEs) is a global concern. In this study, we collected 152 soil samples for evaluation. Considering the contamination factors and using the cumulative index and geostatistical methods, we investigated the contamination levels of PTEs in Baoshan City, China. Using principal component analysis, absolute principal component score-multivariate linear regression, positive matrix factorization, and UNMIX, we analyzed the sources and quantitatively estimated their contributions. The average Cd, As, Pb, Cu, and Zn concentrations were 0.28, 31.42, 47.59, 100.46, and 12.36 mg/kg, respectively. The Cd, Cu, and Zn concentrations exceeded the corresponding background values for Yunnan Province. The combined receptor models showed that natural and agricultural sources contributed primarily to Cd and Cu and As and Pb inputs, accounting for 35.23 and 7.67% pollution, respectively. Industrial and traffic sources contributed primarily to Pb and Zn inputs (47.12%). Anthropogenic activities and natural causes accounted for 64.76 and 35.23% of soil pollution, respectively. Industrial and traffic sources contributed 47.12% to pollution from anthropogenic activities. Accordingly, the control of industrial PTE pollution emissions should be strengthened, and awareness should be raised to protect arable land around roads.

Systematic Evaluation of Two Classical Receptor Models in Source Apportionment of Soil Heavy Metal(loid) Pollution Using Synthetic and Real-World Datasets

Due to the lack of a priori knowledge on true source makeup and contributions, whether the source apportionment results of Unmix and positive matrix factorization (PMF) are accurate cannot be easily assessed, despite the availability of built-in indicators for their goodness of fit and robustness. This study systematically evaluated, for the first time, the applicability and reliability of these models in source apportionment of soil heavy metal(loid)s with synthetic datasets generated using known source profiles and contributions and a real-world dataset as well. For eight synthetic datasets with different pollution source characteristics, feasible Unmix solutions were close to the true source component compositions (R² > 0.936; total mean squared errors (MSEs) < 0.04), while those of PMF had significant deviations (R² of 0.484-0.998; total MSEs of 0.04-0.16). Nonetheless, both models failed to accurately apportion the sources with collinearity or non-normal distribution. Unmix generally outperformed PMF, and its solutions showed much less dependence on sample size than those of PMF. While the built-in indicators provided little hint on the reliability of both models for the real-world dataset, their sample-size dependence indicated that Unmix probably yielded more accurate solutions. These insights could help avoid the potential misuse of Unmix and PMF in source apportionment of soil heavy metal(loid) pollution.

[Improved Performance of PMF Source Apportionment for Volatile Organic Compounds Based on Classification of VOCs' Aging Degree in Air Mass]

VOCs are the key precursors of ozone and secondary organic aerosols. The results of source apportionment for VOCs are very important for the coordinated control of ozone and second organic particulate matter. However, VOCs do not fully meet the assumption of the receptor model because the VOCs released from each source are relatively unstable in the transmission process for their reactivity. As a result, we do not accurately obtain the actual source contribution when the receptor model is used for the source apportionment of VOCs. In order to solve the problem that the relative changes in the components caused by VOCs reactivity are not consistent with the PMF model hypothesis, the aging degree of VOCs was introduced to distinguish the state characteristics after their photochemical reactions in the ambient air. According to the ratio of ethylbenzene to m/p-xylene, VOCs monitored at Wuhai were divided into three aging states:high, medium, and low. The results showed that the model parameters, such as regression equation parameters (slope and intercept), standard error, determination coefficient, and pass rate of residual error, were improved obviously compared to the sample set after classification. Because the degree of aging is closely related to the transport time of air mass and the atmospheric oxidation in the atmosphere, it also reflects the different sources of air mass to some extent. In the high-aging VOCs samples, the coking source occupied a high proportion (up to 47.20%). In the low-aging VOCs samples, the combustion source and coking source accounted for a higher proportion, 28.67% and 24.39%, respectively. After the classification according to the aging degree, the results of VOCs source apportionment by PMF are more consistent with the actual contribution of emission sources.

[Assessment and Source Analysis of Heavy Metal Pollution in Farmland Soils in Southern Youyang County, Chongqing]

To investigate the impact of mining activities and geological background on the soil environment, 156 soil samples were collected from an agricultural land in southern Youyang County, Chongqing. The content and pH of heavy metals in the soil were analyzed, and the Nemerow index method was used to evaluate the pollution status of soil heavy metals. The source of soil heavy metals was discussed using the principal component analysis/absolute principal component score (PCA/APCS) receptor model. The results showed that the soil Cd pollution was distributed in a planar shape, while soil Hg mainly appeared as point pollution. The medium-severe soil pollution was mainly distributed at the junction of Tushi Town, Mawang Town, and Longtan Town, where the soil was predominantly acidic and there was a higher risk of crop contamination; the indicator Kriging evaluation results showed that there was a higher probability of soil contamination at the junction of the three towns and the northern part of Tushi Township. The results of the PCA/APCS receptor model analysis showed that the sources of soil As, Cd, Cr, and Ni were mainly controlled by geological background; soil Hg, Pb, and Zn were mainly controlled by mining activities; further, soil Cu was affected by both geological background and mining activities. In addition, the agricultural activities were also one of the sources of soil As, Cd, Pb, Cu, and Zn. The medium-heavy pollution of the soil in the study area was mainly caused by mining activities, while the heavy metal pollution of the soil caused by geological background was mainly light pollution. This study can provide a theoretical basis for the safe use of land and the prevention and control of soil pollution in typical regions.

Biased Opioid Antagonists as Modulators of Opioid Dependence: Opportunities to Improve Pain Therapy and Opioid Use Management

Opioid analgesics are effective pain therapeutics but they cause various adverse effects and addiction. For safer pain therapy, biased opioid agonists selectively target distinct μ opioid receptor (MOR) conformations, while the potential of biased opioid antagonists has been neglected. Agonists convert a dormant receptor form (MOR-μ) to a ligand-free active form (MOR-μ*), which mediates MOR signaling. Moreover, MOR-μ converts spontaneously to MOR-μ* (basal signaling). Persistent upregulation of MOR-μ* has been invoked as a hallmark of opioid dependence. Contrasting interactions with both MOR-μ and MOR-μ* can account for distinct pharmacological characteristics of inverse agonists (naltrexone), neutral antagonists (6β-naltrexol), and mixed opioid agonist-antagonists (buprenorphine). Upon binding to MOR-μ*, naltrexone but not 6β-naltrexol suppresses MOR-μ*signaling. Naltrexone blocks opioid analgesia non-competitively at MOR-μ*with high potency, whereas 6β-naltrexol must compete with agonists at MOR-μ, accounting for ~100-fold lower in vivo potency. Buprenorphine's bell-shaped dose-response curve may also result from opposing effects on MOR-μ and MOR-μ*. In contrast, we find that 6β-naltrexol potently prevents dependence, below doses affecting analgesia or causing withdrawal, possibly binding to MOR conformations relevant to opioid dependence. We propose that 6β-naltrexol is a biased opioid antagonist modulating opioid dependence at low doses, opening novel avenues for opioid pain therapy and use management.

[Pollution Characteristics and Source Apportionment of Fine Particulate Matter in Autumn and Winter in Puyang, China]

As one of the air pollution transmission channels around the beijing-Tianjin-Hebei region, Puyang frequently suffer from severe airpollution in autumn and winter. In order to study the characteristics and main sources of fine particulate matter during these periods, manual membrane sampling of PM_2.5 was conducted at three national control sites from October 15, 2017, to January 13, 2018. Chemical composition analysis was conducted and, combined with a PMF receptor model, source analysis of the fine particles was also undertaken. The results show that the average mass concentration of PM_2.5 in Puyang was 94.16 μg·m^-3 in the autumn and winter of 2017, and Pushuihe station was the most polluted site. During the heating season, the three control stations all recorded the frequent occurrence of severe and serious pollution events, while the frequency of mild pollution events decreased. When heavy pollution events occurred, the concentrations of NO₂ and CO increased significantly. The main components of PM_2.5 were water-soluble ions (52.33%), OCEC (25.32%), and crustal elements (0.08%). The concentrations of NO₃^- were high while the concentrations of SO₄^2- were low. When heavy pollution occurred, the concentrations of water-soluble ions, OC, EC, and K in PM_2.5 increased significantly, while the concentrations of crustal elements decreased. During the sampling period, the conversion ratios of sulfur and nitrogen in Puyang were high and atmospheric oxidation was strong. The transformation of sulfur and nitrogen promoted the occurrence of heavy pollution. Emissions of NO_x, CO, and VOCs were higher in Puyang in 2017, and the source apportionment results showed that the main sources of PM_2.5 in autumn and winter were secondary inorganic salts (37%), industrial sources (16%), secondary organic aerosol (SOA, 14%), biomass combustion (12%), mobile sources (9%), coal burning (7%), and dust (4%). Secondary transformation played an important role in the development of heavy pollution events in Puyang. It is necessary to focus on the control of emissions from industrial sources, biomass combustion, moving source, and civil coal combustion.

Multi-Pharmacophore Modeling of Caspase-3 Inhibitors using Crystal, Dock and Flexible Conformation Schemes

AIM AND OBJECTIVE

Numerous caspase-3 drug discovery projects were found to have relied on single receptor as the template to recognize most promising small molecule candidates using docking approach. Alternatively, some researchers were contingent upon ligand-based alignment to build up an empirical relationship between ligand functional groups and caspase-3 inhibitory activity quantitatively. To connect both caspase-3 receptor details and its inhibitors chemical functionalities, this study was undertaken to develop receptor- and ligand-pharmacophore models based on different conformational schemes.

MATERIAL AND METHODS

A multi-pharmacophore modeling strategy is carried out based on three conformational schemes of pharmacophore hypothesis generation to screen caspase-3 inhibitors from database. The schemes include (i) flexible (conformations unrestricted or flexible during pharmacophore mapping), (ii) dock (conformations obtained using FlexX docking method) and (iii) crystal (extracted from multiple caspase-3-ligand complexes from PDB repository) conformations of query ligands. The pharmacophore models developed using these conformational schemes were then used to identify probable caspase-3 inhibitors from ZINC database.

RESULTS

We noticed better sensitivity with good specificity measures returned by candidate pharmacophore hypotheses across each conformation type and recognized crucial pharmacophore features that enable caspase-3 binding. Pharmacophore modeling based on flexible conformational scheme indicated that the crystal structure 3KJF (AAAADH) is the best receptor structure to perform receptor-based pharmacophore screening of caspase-3 inhibitors. When multiple crystal structures were included, the hypothesis (HAAA) is more generalized. Superimposition of multiple co-crystal ligands from various caspase-3 PDB entries in crystallographic binding mode revealed similar hypothesis (HAAA). Further, FlexX-guided dock conformations of validation dataset showed that the crystal structure 1RE1 is the best-suited for dock-based pharmacophore models. Database screening using these pharmacophore hypotheses identified N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4- yl]benzohydrazide as the probable caspase-3 inhibitors.

CONCLUSION

N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4-yl]benzohydrazide may be tested for caspase-3 inhibition. We believe that potential caspase-3 inhibitors can be recognized efficiently by adapting multi-pharmacophore models in database screening.

[Effect of a Mining and Smelting Plant on the Accumulation of Heavy Metals in Soils in Arid Areas in Xinjiang]

Arid and semi-arid regions in West China are ecologically fragile zones. Increasing attention has been focused on soil pollution triggered by mining and smelting in those areas. Nine heavy metals in the soil around a mining and smelting plant in Xinjiang were investigated using multivariate analysis, the geoaccumulation index, and GIS techniques. The heavy metals Cu and As were identified as the main pollutants in the study area. The accumulation of the heavy metals Cr, Zn, Ni, and Cd is small and weakly disturbed by human activity. Anthropogenic accumulation of Co and Pb was observed at a few sampling sites; its degree was also small. Anthropogenic accumulation of Mn in soil was not apparent. The factor analysis indicates two sources for the nine heavy metals in the soils. Source 1 includes As, Cu, Ni, Cr, Zn, Cd, and Co, while Source 2 includes Mn and Pb. The spatial distribution suggests that the sites with the highest As, Cu, Ni, Cr, Zn, Pb, Cd, and Co concentrations are in areas close to the tailing dump. The sealing tailing dump is the prior way to prevent the spread of heavy metals. The results also reveal that the PCA/APCS receptor model is not applicable for the quantification of the contribution of heavy metals in soils in this case.

[Contamination Levels and Source Analysis of Heavy Metals in the Finer Particles of Urban Road Dust from Xi'an, China]

Road dust samples were collected from four different functional areas in Xi'an City, i.e., an educational area, a residential area, a park area, and a traffic area, to study the influence of intensive human activities on local urban environmental quality. The contents of Cu, Pb, Zn, Cr, Co, V, Mn, and Ni in the smaller than 63 μm road dust particles were determined by X-ray fluorescence spectrometry, and the pollution levels of these metals were assessed by a geoaccumulation index and a pollution loading index. The possible sources of heavy metals measured in the dusts were identified by multivariate statistical analysis methods, including correlation analysis, principal component analysis, and cluster analysis, and the contributions of each source to heavy metals in the dusts were apportioned by a principal component analysis-multiple linear regression receptor model. The results showed that the contents of Cu, Pb, Zn, Cr, Co, V, Mn, and Ni in the smaller than 63 μm road dust particles of urban road dust from Xi'an ranged 14.2-96.9, 23.5-206.1, 20.0-899.4, 122.7-262.8, 7.9-14.2, 48.7-71.5, 274.0-448.9, and 22.4-62.5 mg·kg^-1, respectively, with averages of 46.6, 97.4, 169.2, 177.5, 9.8, 57.1, 337.6, and 29.3 mg·kg^-1. Compared to the element background values of Shaanxi soil, the finer particles of road dust from Xi'an had elevated contents of Cu, Pb, Zn, and Cr. The finer particles of road dust from Xi'an were unpolluted by Co, V, Mn, and Ni; unpolluted to moderately polluted by Cr, Cu, and Zn; and moderately polluted by Pb. The assessment results of comprehensive pollution indicated that the pollution levels of the heavy metals in the dusts were mainly unpolluted to moderately polluted. The multivariate statistical analysis results displayed that Cr, Cu, Pb, and Zn had significant positive correlation. These metals belong to a principal component and a cluster, whereas Mn, Ni, V, and Co belong to another principal component and cluster and have significant positive correlation. Considering the content characteristics of heavy metals in the dusts, these results illustrated that two kinds of sources for the heavy metals studied existed for the finer particles of road dust from Xi'an, i.e., Cu, Pb, Zn, and Cr mainly originated from traffic sources, whereas V, Co, Mn, and Ni were mainly from natural sources. The contributions of traffic sources and natural sources to the heavy metals in the finer particles of the road dust from Xi'an were respectively 56.7% and 43.3%.

Source Apportionment of Polycyclic Aromatic Hydrocarbons in Sediment by the Application of Non-Negative Factor Analysis: A Case Study of Dalian Bay

An improved method, factor analysis with non-negative constraints (FA-NNC) was adopted to apportion the sources of sediment polycyclic aromatic hydrocarbons (PAHs) in Dalian Bay, China. Cosine similarity and Monte Carlo uncertainty analysis were used to assist the FA-NNC source resolution. The results identified three sources for PAHs, which were overall traffic, diesel engine emissions and residential coal combustion. The contributions of these sources were quantified as 78 ± 4.6% from overall traffic, 12 ± 3.2% from diesel engine emissions, and 10 ± 1.9% from residential coal combustion. The results from the Monte Carlo uncertainty analysis indicated that the model was robust and convergent.