Profiles and Source Apportionment of Nonmethane Volatile Organic Compounds in Winter and Summer in Xi’an, China, based on the Hybrid Environmental Receptor Model

Summer and winter campaigns for the chemical compositions and sources of nonmethane hydrocarbons(NMHCs)and oxygenated volatile organic compounds(OVOCs)were conducted in Xi’an.Data from 57 photochemical assessment monitoring stations for NMHCs and 20 OVOC species were analyzed.Significant seasonal differences were noted for total VOC(TVOC,NMHCs and OVOCs)concentrations and compositions.The campaign-average TVOC concentrations in winter(85.3±60.6 ppbv)were almost twice those in summer(47.2±31.6 ppbv).Alkanes and OVOCs were the most abundant category in winter and summer,respectively.NMHCs,but not OVOCs,had significantly higher levels on weekends than on weekdays.Total ozone formation potential was higher in summer than in winter(by 50%)because of the high concentrations of alkenes(particularly isoprene),high temperature,and high solar radiation levels in summer.The Hybrid Environmental Receptor Model(HERM)was used to conduct source apportionment for atmospheric TVOCs in winter and summer,with excellent accuracy.HERM demonstrated its suitability in a situation where only partial source profile data were available.The HERM results indicated significantly different seasonal source contributions to TVOCs in Xi’an.In particular,coal and biomass burning had contributions greater than half in winter(53.4%),whereas traffic sources were prevalent in summer(53.1%).This study’s results highlight the need for targeted and adjustable VOC control measures that account for seasonal differences in Xi’an;such measures should target not only the severe problem with VOC pollution but also the problem of consequent secondary pollution(e.g.,from ozone and secondary organic aerosols).

Administration of soluble activin receptor 2B increases bone and muscle mass in a mouse model of osteogenesis imperfecta

Osteogenesis imperfecta（OI） comprises a group of heritable connective tissue disorders generally defined by recurrent fractures, low bone mass, short stature and skeletal fragility. Beyond the skeletal complications of OI,many patients also report intolerance to physical activity, fatigue and muscle weakness. Indeed, recent studies have demonstrated that skeletal muscle is also negatively affected by OI, both directly and indirectly. Given the well-established interdependence of bone and skeletal muscle in both physiology and pathophysiology and the observations of skeletal muscle pathology in patients with OI, we investigated the therapeutic potential of simultaneous anabolic targeting of both bone and skeletal muscle using a soluble activin receptor 2B（ACVR2B） in a mouse model of type Ⅲ OI（oim）. Treatment of 12-week-old oim mice with ACVR2 B for 4 weeks resulted in significant increases in both bone and muscle that were similar to those observed in healthy,wild-type littermates. This proof of concept study provides encouraging evidence for a holistic approach to treating the deleterious consequences of OI in the musculoskeletal system.

Neuroactive alkaloids that modulate the neuronal nicotinic receptor and provide neuroprotection in an Alzheimer's disease model:the case of Teline monspessulana

Despite the advances in combinatorial or synthetic chemis- try and bioinformatics, recent literature has demonstrated the relevance of nature and biomass as a source of new molecules to treat different pathologies, i.e., bioactive com- pounds obtained from Ecteinascidia turbinate to treat some types of cancer or rapamycin from Streptomyces hygroscop- icus to prevent organ rejection after transplant. This trend will continue simply due to the fact that Mother Nature has been synthesizing molecules for millions of years. In our lab- oratory, we have characterized several compounds obtained from natural sources and that possess important neuronal effects,

Particulate matter geochemistry of a highly industrialized region in the Caribbean:Basis for future toxicological studies

Air pollution has become an important issue,especially in Caribbean urban areas,and,particulate matter(PM)emitted by different natural and anthropogenic sources causes environmental and health issues.In this work,we studied the concentrations of PM_(10) and PM_(2.5) sources in an industrial and port urban area in the Caribbean region of Colombia.PM samples were collected within 48-h periods between April and October 2018 by using a Partisol 2000 i-D sampler.Elemental geochemical characterization was performed by X-ray fluorescence(XRF)analysis.Further,ionic species and black carbon(BC)were quantified by ion chromatography and reflectance spectroscopy,respectively.Using the Positive Matrix Factorization(PMF)receptor model,the contributions of PM sources were quantified.The average concentration of PM_(10) was 46.6±16.2μg/m^(3),with high concentrations of Cl and Ca.For PM_(2.5),the average concentration was 12.0±3.2μg/m^(3),and the most abundant components were BC,S,and Cl.The receptor model identified five sources for PM_(10) and PM_(2.5).For both fractions,the contributions of marine sea spray,re-suspended soil,and vehicular traffic were observed.In addition,PM_(2.5) included two mixed sources were found to be fuel oil combustion with fertilizer industry emissions,and secondary aerosol sources with building construction emissions.Further,PM_(10) was found to also include building construction emissions with re-suspended soil,and metallurgical industry emissions.These obtained geochemical atmospheric results are important for the implementation of strategies for the continuous improvement of the air quality of the Caribbean region.

Assessing the impact of air pollution sources in Hong Kong, a densely populated city

As the simple dispersion model tested is not sufficiently accurate to predict the SO2 concentration in complex terrain commonly encountered amongst high rise buildings in Hong Kong, and the lack of comprehensive local wind data at most sites prevents the use of more advanced dispersion model to assess the impact of major air pollutant sources, a receptor model approach is adopted for the apportionment of air pollution sources in Hong Kong. The preliminary results obtained are presented and discussed.

Application of Electron Microscopic on the Sources of PM_(10) in the Atmosphere

[ Objective] The aim was to study the application of scanning electron microscopy（SEM） on the physical property and ideological distri- bution of partials. [ Method] By dint of scanning electron microscopy, the morphological property of each source and main elements were analyzed. Compared with morphological property of sampling point, the source of particles was determined. [ Result] The results were consistent with the analysis of CMB8.2 chemical mass balance receptor model. Taking the four detection stations in Longyan Normal Training College, Minxi Vocational ＆ Technical College, Longyan University, and Longyan Environment Monitoring Station as examples, the major air pollutants respectively were soil sand, dust of burning coal, second fugitive dust; soil sand, second fugitive dust, vehicle exhausts; second fugitive dust, soil; and second fugitive dust, vehicle exhausts, dust of burning coal. [ Conclusion] The study result had certain guiding significance to the analysis of sources of particles in the atmosphere and atmosphere environment treatment.

In Silico Investigation of Agonist Activity of a Structurally Diverse Set of Drugs to hPXR Using HM-BSM and HM-PNN

The human pregnane X receptor（hPXR） plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards h PXR. Heuristic method（HM）-Best Subset Modeling（BSM） and HM-Polynomial Neural Networks（PNN） were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain（AD） of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved（for HM-BSM, r^2=0.881, q^2_（LOO）=0.797, q^2_（EXT）=0.674; for HM-PNN, r^2=0.882, q^2_（LOO）=0.856, q^2_（EXT）=0.655）. The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to h PXR.

PM_(2.5) source apportionment in a French urban coastal site under steelworks emission influences using constrained non-negative matrix factorization receptor model

The constrained weighted-non-negative matrix factorization（CW-NMF） hybrid receptor model was applied to study the influence of steelmaking activities on PM_（2.5）（particulate matter with equivalent aerodynamic diameter less than 2.5 μm） composition in Dunkerque,Northern France. Semi-diurnal PM_（2.5）samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals, water-soluble ions, and total carbon using inductively coupled plasma – atomic emission spectrometry（ICP-AES）,ICP- mass spectrometry（ICP-MS）, ionic chromatography and micro elemental carbon analyzer. The elemental composition shows that NO_3^-, SO_4^（2-）, NH_4~＋and total carbon are the main PM_（2.5）constituents. Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced. The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios. Moreover Rb/Cr, Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions. The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation. Eleven source profiles with various contributions were identified： 8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities. Between them,secondary nitrates, secondary sulfates and combustion profiles give the highest contributions and account for 93% of the PM_（2.5）concentration. The steelwork facilities contribute in about 2%of the total PM_（2.5）concentration and appear to be the main source of Cr, Cu, Fe, Mn, Zn.

Source apportionment of polycyclic aromatic hydrocarbons(PAHs) in a sediment core from Lake Dagze Co, Tibetan Plateau, China: Comparison of three receptor models

Receptor models are a useful tool for identifying sources of polycyclic aromatic hydrocarbons(PAHs) in multiple environmental media. In this study, three different receptor models(including the principal component analysis-multiple linear regression(PCA-MLR), positive matrix factorization(PMF), and Unmix models) were used to apportion the sources of 16 priority PAHs in a sediment core of Lake Dagze Co. The ΣPAHs(sum of all 16 measured PAHs) concentrations ranged from 51.89 to 132.82 ng/g with an average of 80.39 ng/g. TheΣ PAHs were dominated by 2-3 ring PAHs, accounting for 80.12% on average, thereby indicating that they mainly originated from biomass and coal combustion and/or from long-range atmospheric transportation. The three models produced consistent source apportionment results. The greatest contributor to ΣPAHs was biomass combustion, followed by coal combustion, vehicle emissions, and petrogenic sources. Moreover, the temporal variation of the common sources was well-correlated among models. The multi-method comparison and evaluation results showed that all three models were useful tools for source apportionment of PAHs, with the PMF model providing better results than the PCA-MLR and Unmix models. The temporal trends of factor contributions were verified by PAHs with different ring numbers. Significant correlations were found between the simulated concentrations of each source factor and the PAHs with different ring numbers(P<0.01), except for the petrogenic source identified by the Unmix model(P>0.05). This study can provide useful information for further investigation of source apportionment of PAHs in the sediment cores.

A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter

Community Multi-Scale Air Quality （CMAQ） estimates of sulfates, nitrates, ammonium, and organic carbon are highly influenced by uncertainties in modeled secondary formation processes, such as chemical mechanisms, volatilization, and condensation rates. These compounds constitute the majority ofPM2.5 mass, and reducing bias in estimated concentrations has benefits for policy measures and epidemiological studies. In this work, a method for adjusting source impacts on secondary species is developed that provides estimates of source contributions and reduces bias in modeled concentrations compared to observations. The bias correction adjusts concentrations and source impacts based on the difference between modeled concentrations and observations while taking into account uncertainties at the location of interest; and it is applied both spatially and temporally. We apply the method over the US for 2006. The mean bias for initial CMAQ concentrations compared to observations is -0.28 （OC）, 0.11 （NO3）, 0.05 （NH4）, and -0.08 （SO4）. The normalized mean bias in modeled concentrations compared to observations was effectively zero for OC, NO3, NH4, and SO4 after applying the secondary bias correction. Ten-fold cross-validation was conducted to determine the performance of the spatial application of the bias correction. Cross-validation performance was favorable; correlation coefficients were greater than 0.69 for all species when comparing observations and concentrations based on kriged correction factors. The methods presented here address model uncertainties by improving simulated concentrations and source impacts of secondary particulate matter through data assimilation. Secondary-adjusted concentrations and source impacts from 20 emissions sources are generated for 2006 over continental US.

Chemical characteristics and source apportionment of PM_(10) during a brown haze episode in Harbin, China

This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.

Source apportionment of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF

Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores （PCA/APCS） and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization （PMF） receptor model to investigate each model＇s source-apportioning capability. All models used the PM10 chemical composition （organic carbon （OC）, elemental carbon （EC）, water soluble inorganic ions （WSIC）, and trace elements） for source apportionment. The average PM10 concentration during the study period was 249.7±103.9 μg/m3 （range： 61.4-584.8 μg/m3）. The UNMIX model resolved five sources （soil dust （SD）, vehicular emissions （VE）, secondary aerosols （SA）, a mixed source of biomass burning （BB） and sea salt （SS）, and industrial emissions （IE））. The PCA/APCS model also resolved five sources, two of which also included mixed sources （SD, VE, SD＋SS, （SA＋BB＋SS） and 1E）. The PMF analysis differentiated seven individual sources （SD, VE, SA, BB, SS, IE, and fossil fuel combustion （FFC））. All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.