Mass, composition, and sources of particulate matter in residential and traffic sites of an urban environment.

Present study aims to assess the mass, composition, and sources of PM10 and PM2.5 (particulate matter having aerodynamic diameter less than or equal to 10 and 2.5 µm aerodynamic diameter, respectively) in Vellore city. Seasonal samples collected in traffic and residential sites were analyzed for ions, elements, organic carbon (OC), and elemental carbon (EC). Source apportionment of PM10 and PM2.5 is carried out using Chemical Mass Balance, Unmix, Positive Matrix Factorization and Principal Component Analysis receptor models. Results showed that traffic site had higher annual concentration (PM2.5 = 62 ± 32 and PM10 = 112 ± 23 µg m-3) when compared to residential site (PM2.5 = 54 ± 22 and PM10 = 98 ± 20 µg m-3). Al, Ca, Fe, K, and Mg known to have crustal origin dominated the element composition irrespective of PM size and sampling site. Among ions, SO42- accounted highest in both sites with an average of 70 and 60% to PM2.5 and PM10 ionic mass. Elemental carbon contribution to PM mass was found highest in traffic site (PM2.5 = 17 to 23% and PM10 = 8 to 10%) than residential site (PM2.5 = 9 to 17% and PM10 = 4 to 8%). Elements, ions, OC, and EC accounted 12, 28, 34, and 16% of PM2.5 mass and 12, 21, 20, and 8% of PM10 mass, respectively. Different sources identified by the receptor models are resuspended road dust, crustal material, secondary aerosol, traffic, non-exhaust vehicular emissions, secondary nitrate, construction, cooking, and biomass burning. Since Vellore is aspiring to be a smart city, this study can help the policymakers in effectively curbing PM.

New 8-amino-1,2,4-triazolo[4,3-a]pyrazin-3-one derivatives. Evaluation of different moieties on the 6-aryl ring to obtain potent and selective human A2A adenosine receptor antagonists.

In this work, further structural investigations on the 8-amino-2-phenyl-6-aryl-1,2,4-triazolo[4,3-a]pyrazin-3-one series were carried out to achieve potent and selective human A2A adenosine receptor (AR) antagonists. Different ether and amide moieties were attached at the para-position of the 6-phenyl ring, thus leading to compounds 1-9 and 10-18, respectively. Most of these moieties contained terminal basic rings (pyrrolidine, morpholine, piperidine and substituted piperazines) which were thought to confer good physicochemical and drug-like properties. Compounds 11-16, bearing the amide linker, possessed high affinity and selectivity for the hA2A AR (Ki = 3.6-11.8 nM). Also derivatives 1-9, featuring an ether linker, preferentially targeted the hA2A AR but with lower affinity, compared to those of the relative amide compounds. Docking studies, carried out at the hA2A AR binding site, highlighted some crucial ligand-receptor interactions, particularly those provided by the appended substituent whose nature deeply affected hA2A AR affinity.

Novel 8-amino-1,2,4-triazolo[4,3-a]pyrazin-3-one derivatives as potent human adenosine A1 and A2A receptor antagonists. Evaluation of their protective effect against ß-amyloid-induced neurotoxicity in SH-SY5Y cells.

In this work, an enlarged series of 1,2,4-triazolo[4,3-a]pyrazin-3-ones was designed to target the human (h) A2A adenosine receptor (AR) or both hA1 and hA2A ARs. The novel 8-amino-1,2,4-triazolopyrazin-3-one derivatives 1-25 featured a phenyl or a benzyl pendant at position 2 while different aryl/heteroaryl substituents were placed at position 6. Two compounds (8 and 10) endowed with high affinity (Ki = 7.2 and 10.6 nM) and a complete selectivity for the hA2A AR were identified. Moreover, several derivatives possessed nanomolar affinity for both hA1 and hA2A ARs (both Ki < 20 nM) and different degrees of selectivity versus the hA3 AR. Two selected compounds (10 and 25) demonstrated ability in preventing ß-amyloid peptide (25-35)-induced neurotoxicity in SH-SY5Y cells. Results of docking studies at the hA2A and hA1 AR crystal structures helped us to rationalize the observed affinity data and to highlight that the steric hindrance of the substituents at the 2- and 6-position of the bicyclic core affects the binding mode in the receptor cavity.

Bacterial peptidoglycan with amidated meso-diaminopimelic acid evades NOD1 recognition: an insight into NOD1 structure-recognition.

Nucleotide-binding oligomerization domain-containing protein 1 (NOD1) is an intracellular pattern recognition receptor that recognizes bacterial peptidoglycan (PG) containing meso-diaminopimelic acid (mesoDAP) and activates the innate immune system. Interestingly, a few pathogenic and commensal bacteria modify their PG stem peptide by amidation of mesoDAP (mesoDAPNH2). In the present study, NOD1 stimulation assays were performed using bacterial PG containing mesoDAP (PGDAP) and mesoDAPNH2 (PGDAPNH2) to understand the differences in their biomolecular recognition mechanism. PGDAP was effectively recognized, whereas PGDAPNH2 showed reduced recognition by the NOD1 receptor. Restimulation of the NOD1 receptor, which was initially stimulated with PGDAP using PGDAPNH2, did not show any further NOD1 activation levels than with PGDAP alone. But the NOD1 receptor initially stimulated with PGDAPNH2 responded effectively to restimulation with PGDAP The biomolecular structure-recognition relationship of the ligand-sensing leucine-rich repeat (LRR) domain of human NOD1 (NOD1-LRR) with PGDAP and PGDAPNH2 was studied by different computational techniques to further understand the molecular basis of our experimental observations. The d-Glu-mesoDAP motif of GMTPDAP, which is the minimum essential motif for NOD1 activation, was found involved in specific interactions at the recognition site, but the interactions of the corresponding d-Glu-mesoDAP motif of PGDAPNH2 occur away from the recognition site of the NOD1 receptor. Hot-spot residues identified for effective PG recognition by NOD1-LRR include W820, G821, D826 and N850, which are evolutionarily conserved across different host species. These integrated results thus successfully provided the atomic level and biochemical insights on how PGs containing mesoDAPNH2 evade NOD1-LRR receptor recognition.

The role of 5-arylalkylamino- and 5-piperazino- moieties on the 7-aminopyrazolo[4,3-d]pyrimidine core in affecting adenosine A1 and A2A receptor affinity and selectivity profiles.

New 7-amino-2-phenylpyrazolo[4,3-d]pyrimidine derivatives, substituted at the 5-position with aryl(alkyl)amino- and 4-substituted-piperazin-1-yl- moieties, were synthesized with the aim of targeting human (h) adenosine A1 and/or A2A receptor subtypes. On the whole, the novel derivatives 1-24 shared scarce or no affinities for the off-target hA2B and hA3 ARs. The 5-(4-hydroxyphenethylamino)- derivative 12 showed both good affinity (Ki = 150 nM) and the best selectivity for the hA2A AR while the 5-benzylamino-substituted 5 displayed the best combined hA2A (Ki = 123 nM) and A1 AR affinity (Ki = 25 nM). The 5-phenethylamino moiety (compound 6) achieved nanomolar affinity (Ki = 11 nM) and good selectivity for the hA1 AR. The 5-(N4-substituted-piperazin-1-yl) derivatives 15-24 bind the hA1 AR subtype with affinities falling in the high nanomolar range. A structure-based molecular modeling study was conducted to rationalize the experimental binding data from a molecular point of view using both molecular docking studies and Interaction Energy Fingerprints (IEFs) analysis.[Formula: see text].

Exploring the 2- and 5-positions of the pyrazolo[4,3-d]pyrimidin-7-amino scaffold to target human A1 and A2A adenosine receptors.

A new series of 7-aminopyrazolo[4,3-d]pyrimidine derivatives (1-31) were synthesized to evaluate some structural modifications at the 2- and 5-positions aimed at shifting affinity towards the human (h) A2A adenosine receptor (AR) or both hA2A and hA1 ARs. The most active compounds were those featured by a 2-furyl or 5-methylfuran-2-yl moiety at position 5, combined with a benzyl or a substituted-benzyl group at position 2. Several of these derivatives (22-31) displayed nanomolar affinity for the hA2A AR (Ki=3.62-57nM) and slightly lower for the hA1 ARs, thus showing different degrees (3-22 fold) of hA2A versus hA1 selectivity. In particular, the 2-(2-methoxybenzyl)-5-(5-methylfuran-2-yl) derivative 25 possessed the highest hA2A and hA1 AR affinities (Ki=3.62nM and 18nM, respectively) and behaved as potent antagonist at both these receptors (cAMP assays). Its 2-(2-hydroxybenzyl) analog 26 also showed a high affinity for the hA2A AR (Ki=5.26nM) and was 22-fold selective versus the hA1 subtype. Molecular docking investigations performed at the hA2A AR crystal structure and at a homology model of the hA1 AR allowed us to represent the hypothetical binding mode of our derivatives and to rationalize the observed SARs.

Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago.

PM2.5 concentrations throughout much of the U.S. have decreased over the last 15 years, but emissions and concentration trends can vary by location and source type. Such trends should be understood to inform air quality management and policies. This work examines trends in emissions, concentrations and source apportionments in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. Annual and seasonal trends were investigated using National Emission Inventory (NEI) data for 2002 to 2011, speciated ambient PM2.5 data from 2001 to 2014, apportionments from positive matrix factorization (PMF) receptor modeling, and quantile regression. Over the study period, county-wide data suggest emissions from point sources decreased (Detroit) or held constant (Chicago), while emissions from on-road mobile sources were constant (Detroit) or increased (Chicago), however changes in methodology limit the interpretation of inventory trends. Ambient concentration data also suggest source and apportionment trends, e.g., annual median concentrations of PM2.5 in the two cities declined by 3.2 to 3.6 %/yr (faster than national trends), and sulfate concentrations (due to coal-fired facilities and other point source emissions) declined even faster; in contrast, organic and elemental carbon (tracers of gasoline and diesel vehicle exhaust) declined more slowly or held constant. The PMF models identified nine sources in Detroit and eight in Chicago, the most important being secondary sulfate, secondary nitrate and vehicle emissions. A minor crustal dust source, metals sources, and a biomass source also were present in both cities. These apportionments showed that the median relative contributions from secondary sulfate sources decreased by 4.2 to 5.5% per year in Detroit and Chicago, while contributions from metals sources, biomass sources, and vehicles increased from 1.3 to 9.2% per year. This first application of quantile regression to trend analyses of speciated PM2.5 data reveals that source contributions to PM2.5 varied as PM2.5 concentrations decreased, and that the fraction of PM2.5 due to emissions from vehicles and other local emissions has increased. Each data source has uncertainties, but emissions, monitoring and PMF data provide complementary information that can help to discern trends and identify contributing sources. Study results emphasize the need to target specific sources in policies and regulations aimed at decreasing PM2.5 concentrations in urban areas.

Investigating the differences between receptor and dispersion modeling for concentration prediction and health risk assessment of volatile organic compounds from petrochemical industrial complexes.

Receptor and dispersion models both provide important information to help understand the emissions of volatile organic compounds (VOCs) and develop effective management strategies. In this study, differences between the predicted concentrations of two models and the associated impacts on the estimated health risks due to different theories behind two models were investigated. Two petrochemical industrial complexes in Kaohsiung city of southern Taiwan were selected as the sites for this comparison. Although the study compares the approaches by applying the methods to this specific area, the results are expected to be adopted for other areas or industries. Ninety-nine VOC concentrations at eight monitoring sites were analyzed, with the effects of diurnal temperature and seasonal humidity variations being considered. The Chemical Mass Balance (CMB) receptor model was used for source apportionment, while the Industrial Source Complex (ISC) dispersion model was used to predict the VOC concentrations at receptor sites. In the results of receptor modeling, 54% ± 11% and 49% ± 20% of the monitored concentrations were contributed by process emissions in two complexes, whereas the numbers increased to 78% ± 41% and 64% ± 44% in the results of dispersion modeling. Significant differences were observed between two model predictions (p < 0.05). The receptor model was more reproducible given the smaller variances of its results. The effect of seasonal humidity variation on two model predictions was not negligible. Similar findings were observed given that the cancer and non-cancer risks estimated by the receptor model were lower but more reproducible. The adverse health risks estimated by the dispersion model exceeded and were 75.3%-132.4% of the values estimated by using the monitored data, whereas the percentages were lowered to the range from 27.4% to 53.8% when the prediction was performed by using the receptor model. As the results of different models could be significantly different and affect the final health risk assessment, it is important to carefully choose an appropriate model for prediction and to evaluate by monitoring to avoid providing false information for appropriate management.

PM2.5 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 PM2.5 (particulate matter with equivalent aerodynamic diameter less than 2.5 µm) composition in Dunkerque, Northern France. Semi-diurnal PM2.5 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 NO3(-), SO4(2-), NH4(+) and total carbon are the main PM2.5 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 PM2.5 concentration. The steelwork facilities contribute in about 2% of the total PM2.5 concentration and appear to be the main source of Cr, Cu, Fe, Mn, Zn.

1,2,4-triazolo[1,5-a]quinoxaline derivatives and their simplified analogues as adenosine A3 receptor antagonists. Synthesis, structure-affinity relationships and molecular modeling studies.

The 1,2,4-triazolo[1,5-a]quinoxaline (TQX) scaffold was extensively investigated in our previously reported studies and recently, our attention was focused at position 5 of the tricyclic nucleus where different acyl and carboxylate moieties were introduced (compounds 2-15). This study produced some interesting compounds endowed with good hA3 receptor affinity and selectivity. In addition, to find new insights about the structural requirements for hA3 receptor-ligand interaction, the tricyclic TQX ring was destroyed yielding some 1,2,4-triazole derivatives (compounds 16-23). These simplified compounds, though maintaining the crucial structural requirements for adenosine receptor-ligand interaction, have a very low hA3 adenosine receptor affinity, the only exception being compound 23 (1-[3-(4-methoxyphenyl)-1-phenyl-1H-1,2,4-triazol-5-yl]-3-phenylurea) endowed with a Ki value in the micro-molar range and high hA3 selectivity versus both hA1 and hA2A AR subtypes. Evaluation of the side products obtained in the herein reported synthetic pathways led to the identification of some new triazolo[1,5-a]quinoxalines as hA3AR antagonists (compounds 24-27). These derivatives, though lacking the classical structural requirements for the anchoring at the hA3 receptor site, show high hA3 affinity and in some case selectivity versus hA1 and hA2A subtypes. Molecular docking of the herein reported tricyclic and simplified derivatives was carried out to depict their hypothetical binding mode to our model of hA3 receptor.

Predicting latent source-specific PM2.5 pollution from regional sources at unmonitored sites by Bayesian spatial multivariate receptor modeling.

Fine particulate matter (PM2.5) has been a pollutant of main interest globally for more than two decades, owing to its well-known adverse health effects. For developing effective management strategies for PM2.5, it is vital to identify its major sources and quantify how much they contribute to ambient PM2.5 concentrations. With the expanded monitoring efforts established during recent decades in Korea, speciated PM2.5 data needed for source apportionment of PM2.5 are now available for multiple sites (cities). However, many cities in Korea still do not have any speciated PM2.5 monitoring station, although quantification of source contributions for those cities is in great need. While there have been many PM2.5 source apportionment studies throughout the world for several decades based on monitoring data collected from receptor site(s), none of those receptor-oriented studies could predict unobserved source contributions at unmonitored sites. This study predicts source contributions of PM2.5 at unmonitored locations using a recently developed novel spatial multivariate receptor modeling (BSMRM) approach, which incorporates spatial correlation in data into modeling and estimation for spatial prediction of latent source contributions. The validity of BSMRM results is also assessed based on the data from a test site (city), not used in model development and estimation.

[Pollution Characteristics and Source Apportionment of Heavy Metals in Farmland Soils Around the Gangue Heap of Coal Mine Based on APCS-MLR and PMF Receptor Model].

To analyze the pollution characteristics and source apportionment of heavy metal pollution in soil of farmland surrounding the Gangue Heap of Coal Mine in Nanchuan, Chongqing, the Nemerow pollution index and Muller index were used. Meanwhile, to investigate the sources and contribution rate of heavy metals in the soil, absolute principal component score-multiple linear regression receptor modeling (APCS-MLR) and positive matrix factorization (PMF) were employed. The results showed higher amounts of Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn in the downstream area than those in the upstream area, and only Cu, Ni, and Zn showed significantly higher amounts in the downstream area than those in upstream area (P<0.05). The comprehensive Nemerow pollution index was as follows:downstream area (1.22)>upstream area (0.95), and the degree of heavy metal pollution was:Cd>Cu>Hg, As, Pb, Cr, Ni, and Zn. The Muller pollution index showed:Cd>As>Cu=Hg>Ni>Zn=Cr>Pb. The pollution source analysis showed that Cu, Ni, and Zn were mainly affected by mining activities such as long-term accumulation of the gangue heap of coal mine, with the contribution rates of APCS-MLR being 49.8%, 94.5%, and 73.2%, respectively. Additionally, PMF contribution rates were 62.8%, 62.2%, and 63.1%, respectively. Cd, Hg, and As were mainly affected by agricultural activities and transportation activities, with APCS-MLR contribution rates of 49.8%, 94.5%, and 73.2% and PMF contribution rates of 62.8%, 62.2%, and 63.1%, respectively. Further, Pb and Cr were mainly affected by natural factors, with APCS-MLR contribution rates of 66.4% and 94.7% and PMF contribution rates of 42.7% and 47.7%, respectively. The results of source analysis were basically consistent between the APCS-MLR and PMF receptor models.

Generalised non-negative matrix factorisation for air pollution source apportionment.

Source Apportionment (SA) techniques are widely used for identifying key sources of air pollution, thereby providing critical inputs for policy measures. Positive Matrix Factorisation (PMF) (Paatero and Tapper, 1994) is a widely used SA technique. PMF uses the speciated concentration data (X) collected over several days and factorises it into source contribution (G) and source profile (F) matrices, albeit under positivity constraint. Towards this end, it involves solving an optimisation problem where the elements of X are weighted by the inverse of the standard deviations of the corresponding errors introduced during the sampling and chemical analysis process. Thus, PMF implicitly assumes that the errors in different elements of the X matrix are uncorrelated. This assumption may not hold since the sampling, and chemical analysis steps deployed in any data-collection campaign will inevitably lead to correlated errors. While there are other existing Non-Negative Matrix Factorisation (NMF) methods in literature that can be potentially used for SA, these also make various restrictive assumptions about the error covariance structure. In this work, we propose a new method called Generalised Non-Negative Matrix Factorisation (GNMF) to fill this gap. In particular, the proposed method is able to incorporate any error covariance matrix without making any restrictive assumptions on its structure. Towards this end, we integrate the full error covariance matrix in the objective function to be minimised to obtain F and G matrices. We derive the corresponding update rules for obtaining these matrices iteratively. To ensure non-negativity, we extend the multiplicative and projected gradient-based ideas available in NMF literature to the proposed GNMF approach. The proposed method subsumes various NMF methods available in literature as special cases. The utility of the proposed approach is demonstrated by comparing its performance with other methods on an SA problem using a dataset derived from field measurements.

4-Heteroaryl Substituted Amino-3,5-Dicyanopyridines as New Adenosine Receptor Ligands: Novel Insights on Structure-Activity Relationships and Perspectives.

A new set of amino-3,5-dicyanopyridines was synthesized and biologically evaluated at the adenosine receptors (ARs). This chemical class is particularly versatile, as small structural modifications can influence not only affinity and selectivity, but also the pharmacological profile. Thus, in order to deepen the structure-activity relationships (SARs) of this series, different substituents were evaluated at the diverse positions on the dicyanopyridine scaffold. In general, the herein reported compounds show nanomolar binding affinity and interact better with both the human (h) A1 and A2A ARs than with the other subtypes. Docking studies at hAR structure were performed to rationalize the observed affinity data. Of interest are compounds 1 and 5, which can be considered as pan ligands as binding all the ARs with comparable nanomolar binding affinity (A1AR: 1, Ki = 9.63 nM; 5, Ki = 2.50 nM; A2AAR: 1, Ki = 21 nM; 5, Ki = 24 nM; A3AR: 1, Ki = 52 nM; 5, Ki = 25 nM; A2BAR: 1, EC50 = 1.4 nM; 5, EC50 = 1.12 nM). Moreover, these compounds showed a partial agonist profile at all the ARs. This combined AR partial agonist activity could lead us to hypothesize a potential effect in the repair process of damaged tissue that would be beneficial in both wound healing and remodeling.

More than meets the AI: The possibilities and limits of machine learning in olfaction.

Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system's biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd's "Logic of Research Questions," a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question.

Impact of the COVID-19 lockdown on the chemical composition and sources of urban PM2.5.

The lockdown measures caused by the COVID-19 pandemic substantially affected air quality in many cities through reduced emissions from a variety of sources, including traffic. The change in PM2.5 and its chemical composition in downtown Toronto, Canada, including organic/inorganic composition and trace metals, were examined by comparing with a pre-lockdown period and respective periods in the three previous years. During the COVID-19 lockdown, the average traffic volume reduced by 58%, whereas PM2.5 only decreased by 4% relative to the baselines. Major chemical components of PM2.5, such as organic aerosol and ammonium nitrate, showed significant seasonal changes between pre- and lockdown periods. The changes in local and regional PM2.5 sources were assessed using hourly chemical composition measurements of PM2.5. Major regional and secondary PM2.5 sources exhibited no clear reductions during the lockdown period compared to pre-lockdown and the previous years. However, cooking emissions substantially dropped by approximately 61% due to the restrictions imposed on local businesses (i.e., restaurants) during the lockdown, and then gradually increased throughout the recovery periods. The reduction in non-tailpipe emissions, characterized by road dust and brake/tire dust, ranged from 37% to 61%, consistent with the changes in traffic volume and meteorology across seasons in 2020. Tailpipe emissions dropped by approximately 54% and exhibited even larger reductions during morning rush hours. The reduction of tailpipe emissions was statistically associated with the reduced number of trucks, highlighting that a small fraction of trucks contributes disproportionally to tailpipe emissions. This study provides insight into the potential for local benefits to arise from traffic intervention in traffic-dominated urban areas and supports the development of targeted strategies and regulations to effectively reduce local air pollution.

Design and Synthesis of Novel Thiazolo[5,4-d]pyrimidine Derivatives with High Affinity for Both the Adenosine A1 and A2A Receptors, and Efficacy in Animal Models of Depression.

New compounds with a 7-amino-2-arylmethyl-thiazolo[5,4-d]pyrimidine structure were synthesized and evaluated in vitro for their affinity and/or potency at the human (h) A1, hA2A, hA2B, and hA3 adenosine receptors (ARs). Several compounds (5, 8-10, 13, 18, 19) were characterized by nanomolar and subnanomolar binding affinities for the hA1 and the hA2A AR, respectively. Results of molecular docking studies supported the in vitro results. The 2-(2-fluorobenzyl)-5-(furan-2yl)-thiazolo[5,4-d]pyrimidin-7-amine derivative 18 (hA1 Ki = 1.9 nM; hA2A Ki = 0.06 nM) was evaluated for its antidepressant-like activity in in vivo studies, the forced swimming test (FST), the tail suspension test (TST), and the sucrose preference test (SPT) in mice, showing an effect comparable to that of the reference amitriptyline.

Assessment of mobile source contributions in El Paso by PMF receptor modeling coupled with wind direction analysis.

It is well-known that El Paso is the only border area in Texas that has violated national air quality standards. Mobile source emissions (including vehicle exhaust) contribute significantly to air pollution, along with other sources including industrial, residential, and cross-border. This study aims at separating unobserved vehicle emissions from air-pollution mixtures indicated by ambient air quality data. The level of contributions from vehicle emissions to air pollution cannot be determined by simply comparing ambient air quality data with traffic levels because of the various other contributors to overall air pollution. To estimate contributions from vehicle emissions, researchers employed advanced multivariate receptor modeling called positive matrix factorization (PMF) to analyze hydrocarbon data consisting of hourly concentrations measured from the Chamizal air pollution monitoring station in El Paso. The analysis of hydrocarbon data collected at the Chamizal site in 2008 showed that approximately 25% of measured Total Non-Methane Hydrocarbons (TNMHC) was apportioned to motor vehicle exhaust. Using wind direction analysis, researchers also showed that the motor vehicle exhaust contributions to hydrocarbons were significantly higher when winds blow from the south (Mexico) than those when winds blow from other directions. The results from this research can be used to improve understanding source apportionment of pollutants measured in El Paso and can also potentially inform transportation planning strategies aimed at reducing emissions across the region.

Spatially resolved distribution, sources and health risks of heavy metals in size-fractionated road dust from 57 sites across megacity Kolkata, India.

This work reports the first assessment of contamination levels, source contributions and health risks associated with heavy metals (HMs) in road dust from Kolkata, the second-most polluted metropolis in India. To this end, samples collected from 57 locations across 6 land-use categories: residential, roadside, traffic, railway, port and industrial areas in the city during 2018 were analyzed for 11 major and trace metals (Ca, Mg, Fe, Al, Mn, Ni, V, Cu, Zn, Cr, Pb) in three size fractions: <75 µm, 75-125 µm and 125-300 µm. Overall, Mn, Zn, Cr, Pb, V, Cu and Ni were enriched in the smallest fraction by factors of 1.2-2.7. Based on metal distribution across land-use categories, crustal dust (Fe, Al, V), construction activities (Ca, Mg), metallurgical processes (Pb), and non-exhaust abrasive emissions from brake, tire and paint wear (Cu, Zn, Cr) were found to be significant. HMs such as Cu, Zn, Cr and Pb were considerably enriched over background levels as suggested by three contamination indices: Enrichment Factor (EF; overall range: 2.4-12.0), Index of Geo-accumulation (Igeo; overall range: 1.1-3.4), and Pollution Index (PI; overall range: 3.1-15.6). Geospatial mapping identified HM contamination hotspots (integrated PI >4) in west-central and northern parts (the older sections) of the city represented by industrial, port, and traffic-congested residential areas. Using positive matrix factorization (PMF), the following sources were apportioned for the three size fractions: crustal dust (48-66%), construction activities (18-20%), vehicular abrasion (7-21%), industrial emissions (5-8%), a Cr-dominated mixed source (6%) and an unassigned source (7%). Finally, health risk assessment in the form of cumulative hazard index (HI) and incremental lifetime cancer risk (ILCR) found that children (mean HIchildren: 1.29 and ILCRchildren: 2E-04) are comparatively more vulnerable than adults (mean HIadults: 0.22 and ILCRadults: 8E-05) to HM exposure, with the ingestion exposure pathway dominating over dermal contact and inhalation.

Unmix Optimum analysis of PAH sediment sources.

Unmix Optimum (UnmixO) was developed to analyze data, such as sediment PAH data, that were resistant to existing methods of multivariate analysis. Using a geometrical approach, UnmixO uses multiple advanced nonlinear optimization algorithms to find potential sources that obey non-negativity constraints while optimally fitting the data. UnmixO does not require specific knowledge of the uncertainties in the data and will work better for smaller data sets than other multivariate models. UnmixO was able to identify polycyclic aromatic hydrocarbon (PAH) contaminant sources contributing to sediment samples based on sample composition data with good diagnostic values. Results were compared to published EPA Chemical Mass Balance (CMB) sediment results from Lady Bird Lake (LBL) Austin, TX and 40 lakes (40LKS) across the U.S. A Chi-sum approach determined which UnmixO source profile best matched profiles used in CMB sediment studies; two coal tar (CT) sealcoat sources and a mixed combustion source contributed to the sediment PAHs. These results were consistent with CMB results for the LBL and 40LKS studies that estimated CT sealcoats contribute over 80% of PAHs to urban lakes. UnmixO results also showed that CT sealant's contribution to sediments decreased after the City of Austin ban in 2006.