Receptor Model Research Articles

Trace elements in PM2.5 shed light on Saharan dust incursions over the Munich airshed in spring 2022

Munich was persistently affected by Saharan dust in March 2022. PM2.5 was collected on quartz filters and elements were analyzed by ICP-MS. Elemental data were processed by chemometrics and receptor models by Varimax and SOM analysis was applied.

Quantification of sources and potential risks of cadmium, chromium, lead, mercury and arsenic in agricultural soils in a rapidly urbanizing region of southwest China: the case of Chengdu.

Rapid urbanization a major factor affecting heavy metal contamination on suburban agricultural soils. In order to assess the dynamic contamination of heavy metals in soil from agricultural land bordering a rapidly urbanizing area and the transfer of human health risks from contaminants in this process, 186 and 293 soil samples from agricultural land in suburban Chengdu were collected in September 2008 and September 2017, respectively. Several indicators, such as the integrated pollution index (PI) and the potential ecological risk index (RI), were employed for analyzing the heavy metal contamination levels, and the APCS-MLR receptor model were applied for analyzing the heavy metal sources. As a result, mean concentrations for five elements did not exceed the national soil pollution risk screening values in the two periods mentioned above. Nemerow's composite contamination index revealed an increase in soil contamination of arable land after 10 years of urbanization, with 3.75 and 1.02% of light and moderate sample plots, respectively, by 2017. The assessment for potential ecological risk indicated an increased level of eco-risk to high for most of the sample plots. Based on the APCS-MLR model, the origin and contribution to the five elements varied considerably between the two periods mentioned above. Among them, soil Pb changed from "industrial source" to "transportation source," soil Cr changed from "natural source" to "transportation source," and As and Hg changed from "industrial source" to "transportation source." As and Hg were associated with agricultural activities in both periods, and Cd was derived from industrial activities in both periods. The study suggests that inhalation has become a major contributor to non-cancer health risks in urbanization, unlike intake routes in previous periods, and that the increase in cancer risk is mainly due to children's consumption of agricultural products with As residues. The change in the main source of As to "transportation" also indicates a decrease in air quality during urbanization and the development of the transportation industry. This study provides a reference for the governments of rapidly urbanizing cities to formulate relevant highway and agricultural policies to safeguard the health of the people based on the current situation.

Identifying source apportionment of atmospheric particulate matter and gaseous pollutants using receptor models : A case study of Bengaluru, India

The data of Particulate matter PMs (PM2.5, PM10) and Gaseous Pollutants such as carbon monoxide (CO), methane (CH4), oxides of nitrogen (NOx: NO and NO2), non-methane hydrocarbons (NMHCs), sulfur dioxide (SO2), along with ammonia (NH3) at five different locations across Bengaluru from 1st January, 2017 to 20th March, 2018 were collected. The primary objective of this research work is to identify the sources of atmospheric particulate matter and gaseous pollutants using receptor models in Bengaluru, India. To execute this, receptor models, namely Conditional Bivariate Probability Function (CBPF) and Concentrated Weighted Trajectory (CWT) Analysis, are applied. Conditional Bivariate Probability Function (CBPF) shows that, annually, the maximum concentrations of PMs over receptor sites were detected during low wind speed (< 2 knots) along the north-east direction specifying that the long-range transport does not play an essential role in the transportation of higher concentrations of PM and their primary source region may be localized. Concentrated Weighted Trajectory (CWT) analysis shows that, seasonally, the highest air mass contribution of about 37% was noticed in summer, whereas the lowest was in the post-monsoon season (13%). The significant contribution of PM2.5 transported from long distances was during monsoon, and in the case of PM10, it was in summer. The study suggests that the long-range transport of PMs and gaseous Pollutants was not vital and was observed to be localized.

Analysis of the Source Apportionment and Pathways of Heavy Metals in Soil in a Coal Mining Area Based on Machine Learning and an APCS-MLR Model

Long-term coal mining activities have led to severe heavy metal pollution in the soil environment of coal mining areas, posing significant threats to both ecological environments and human health. In this study, surface soil samples were collected from the overlying soil of coal gangue and the surrounding areas of the Panyi coal mine in Huainan. The concentrations of Cd, Zn, Cu, Ni, and Pb elements were determined. A self-organizing map (SOM) and an absolute principal component score multiple linear regression (APCS-MLR) receptor model were employed for the quantitative analysis of the soil’s heavy metal pollution sources and contributions. Additionally, this study focused on the analysis of the pathways of the relatively serious pollution of Cd. The results revealed that the average concentrations of heavy metals (Cd, Pb, Zn, Cu, Cr, and Ni) in the study area were 4.55, 0.59, 1.54, 0.69, 0.59, and 0.71 times the local soil background values, respectively. The concentrations of Cd and Zn exceeded the risk screening values at some sampling points, with exceedance rates of 44% and 8%, respectively, indicating a relatively serious Cd contamination. The sources of heavy metals in the soil in the study area were classified into four categories: mining activities, agricultural activities, weathering of natural matrices, and other unknown sources, with average contributions of 55.48 percent, 24.44 percent, 8.91 percent and 11.86 percent, respectively. Based on the spatial distribution of Cd, it was inferred that atmospheric deposition is one of the important pollution pathways of Cd in the study area. Cd profile distribution patterns and a surface water pollution survey showed that the farmland areas were affected by the irrigation water pathway to some extent. The vertical distribution of heavy metal content in the forest area showed a strong disorder, which was related to the absorption function of plant roots. The results of this study can help to improve the environmental management of heavy metal pollution so as to protect the ecological environment and human health.

High loadings of carbonaceous aerosols from wood smoke in the atmosphere of Beijing from 2015 to 2017: Implications for energy transition policy

Recently, biomass has been regarded as a promising option for solid energy in China, which is promoted in the residential sector and firing power plants. We collected 200 PM2.5 samples (particulate matter with a aerodynamic diameter smaller than 2.5 μm) at multi-sites across Beijing from three individual sampling cases from 2015 to 2017. The levels of OC, OC fractions, EC, EC fractions, as well as K+ were measured. Then, we adopted the Positive Matrix Factorization 5.0 to apportion the sources of carbonaceous aerosols. The source apportionment results were compared with the estimates of source contribution using the bottom-up technical method with the latest emission inventories after the Action Plan was put into effect in 2013. Our results demonstrate that high pollution of carbonaceous aerosols originated from wood smoking based on the receptor modeling and bottom-up technical method in Beijing from 2015 to 2017. Future energy transition policy should focus on the technologies and regulations for reducing emissions from renewable biomass fuel combustion. This study highlights the importance of regulations that address emissions controls on fuels replacing coal combustion to meet the needs to mitigate air pollution from primary energy use.

Synthesis, molecular docking, electrochemical and fluorimetric analysis of new caffeic and cinnamic acid-conjugated hemorphin derivatives designed as potential anticonvulsant and antinociceptive agents

Based on the pharmacophore model of opioid receptors, our team recently synthesized a series of short‐chain hemorphin peptide analogs containing non‐natural amino acids. They demonstrated anticonvulsant and antinociceptive activity with low neurotoxicity. In the present study, a series of novel bioconjugates of N-modified hemorphin analogs containing second pharmacophore cinnamic acids (CA) or caffeic (KA) were synthesized by a traditional solid-phase Fmoc chemistry method for peptide synthesis. Electrochemical and fluorimetric analysis, in vivo anticonvulsant and antinociceptive activity in mice were conducted on the compounds. The three CA acid- (H4-CA, H5-CA, and H7-CA) and three KA acid- (H4-KA, H5-KA, and H7-KA) conjugated hemorphin derivatives exhibited potency at the highest doses of 2 µg/5 µl, administered by intracerebroventricular (icv) mode, against seizure spread in the maximal electroshock test (MES) in mice. The KA-conjugated H5-KA derivate, at the lowest dose, was the only compound that suppressed clonic seizures in the subcutaneous pentylenetetrazol (scPTZ) test. Except for the H5-CA, all tested CA acid- and KA acid-conjugated peptide derivates had the potency to increase the latency for clonic seizures in a dose-dependent mode. The activity against the psychomotor seizures in the 6-Hz test was detected only for the H4-CA (0.5 µg) and H4-KA (0.5 µg and 1 µg), respectively. All investigated peptides showed a more pronounced antinociceptive effect in the “intraplantar formalin” test compared to the “hot plate” test. Shorter chain analogs showed a better antinociceptive profile against tonic pain. The data suggest a DOR and KOR-mediated mechanism of action. According to the docking analysis, H7-CA showed a different antinociceptive profile than other investigated peptides. The novel peptide derivates did not exhibit neurotoxicity in the rotarod test. Our findings suggest that conjugated CA and KA morphine peptides can be used to develop novel morphine-related analogs with anticonvulsant and antinociceptive activity.

A Model of the Full-Length Cytokinin Receptor: New Insights and Prospects.

Cytokinins (CK) are one of the most important classes of phytohormones that regulate a wide range of processes in plants. A CK receptor, a sensor hybrid histidine kinase, was discovered more than 20 years ago, but the structural basis for its signaling is still a challenge for plant biologists. To date, only two fragments of the CK receptor structure, the sensory module and the receiver domain, were experimentally resolved. Some other regions were built up by molecular modeling based on structures of proteins homologous to CK receptors. However, in the long term, these data have proven insufficient for solving the structure of the full-sized CK receptor. The functional unit of CK receptor is the receptor dimer. In this article, a molecular structure of the dimeric form of the full-length CK receptor based on AlphaFold Multimer and ColabFold modeling is presented for the first time. Structural changes of the receptor upon interacting with phosphotransfer protein are visualized. According to mathematical simulation and available data, both types of dimeric receptor complexes with hormones, either half- or fully liganded, appear to be active in triggering signals. In addition, the prospects of using this and similar models to address remaining fundamental problems of CK signaling were outlined.

Source identification and risk assessment of trace metals in surface sediment of China Sea by combining APCA-MLR receptor model and lead isotope analysis

This study aimed to investigate the distribution, pollution, risk and sources of trace metals in sediments along China Sea. Clear spatial variations were found for Cr, Mn, Co, Ni, Cu, Zn, Se, Mo, Ag, Cd, and Pb, whereas As did not show spatial variation. East China Sea (ECS) contained the highest concentrations of Mn, Co, Ni, Cu, Zn, South China Sea (SCS) shallow sea contained the highest concentrations of Zn, Se, Mo, Ag, Cd, and Pb, whereas coral reefs contained the lowest concentrations of trace metals. Spatial variations could be explained by economic development characteristics along China Sea. As, Se and Cd exhibited low to moderate pollution in China Sea sediment, yet pollution for Cu, Zn, Ni, and Ag appeared in some regions. Sediment in ECS had moderate ecological risks and other regions at low ecological risks. The absolute principle component score-multiple linear regression (APCS-MLR) and Pb stable isotope indicated that 43–74% of trace metals (Ni, Cu, Zn, As, Se, Cd, and Pb) were derived from anthropogenic sources like traffic emission, agricultural activities, industrial source. No pollution and ecological risk were observed in coral reefs, yet 39–71% (Pb) was derived from anthropogenic activities such as motor vessels.

Quantitative receptor model for responses that are left- or right-shifted versus occupancy (are more or less concentration sensitive): the SABRE approach.

Simple one-to three-parameter models routinely used to fit typical dose-response curves and calculate EC50 values using the Hill or Clark equation cannot provide the full picture connecting measured response to receptor occupancy, which can be quite complex due to the interplay between partial agonism and (pathway-dependent) signal amplification. The recently introduced SABRE quantitative receptor model is the first one that explicitly includes a parameter for signal amplification (γ) in addition to those for binding affinity (K d), receptor-activation efficacy (ε), constitutive activity (ε R0), and steepness of response (Hill slope, n). It can provide a unified framework to fit complex cases, where fractional response and occupancy do not match, as well as simple ones, where parameters constrained to specific values can be used (e.g., ε R0 = 0, γ = 1, or n = 1). Here, it is shown for the first time that SABRE can fit not only typical cases where response curves are left-shifted compared to occupancy (κ = K d/EC50 > 1) due to signal amplification (γ > 1), but also less common ones where they are right-shifted (i.e., less concentration-sensitive; κ = K d/EC50 < 1) by modeling them as apparent signal attenuation/loss (γ < 1). Illustrations are provided with μ-opioid receptor (MOPr) data from three different experiments with one left- and one right-shifted response (G protein activation and β-arrestin2 recruitment, respectively; EC50,Gprt < K d < EC50,βArr). For such cases of diverging pathways with differently shifted responses, partial agonists can cause very weak responses in the less concentration-sensitive pathway without having to be biased ligands due to the combination of low ligand efficacy and signal attenuation/loss-an illustration with SABRE-fitted oliceridine data is included.

High-time resolution PM2.5 source apportionment assisted by spectrum-based characteristics analysis

Characteristics extraction and anomaly analysis based on frequency spectrum can provide crucial support for source apportionment of PM2.5 pollution. In this study, an effective source apportionment framework combining the Fast Fourier Transform (FFT)- and Continuous Wavelet Transform (CWT)-based spectral analyses and Positive Matrix Factorization (PMF) receptor model is developed for spectrum characteristics extraction and source contribution assessment. The developed framework is applied to Beijing during the winter heating period with 1-h time resolution. The spectrum characteristics of anomaly frequency, location, duration and intensity of PM2.5 pollution can be captured to gain an in-depth understanding of source-oriented information and provide necessary indicators for reliable PMF source apportionment. The combined analysis demonstrates that the secondary inorganic aerosols make relatively high contributions (50.59 %) to PM2.5 pollution during the winter heating period in Beijing, followed by biomass burning, vehicle emission, coal combustion, road dust, industrial process and firework emission sources accounting for 15.01 %, 11.00 %, 10.70 %, 5.31 %, 3.88 %, and 3.51 %, respectively. The source apportionment result suggests that combining frequency spectrum characteristics with source apportionment can provide consistent rationales for understanding the temporal evolution of PM2.5 pollution, identifying the potential source types and quantifying the related contributions.

Characterization of urban aerosol pollution before and during the COVID-19 crisis in a central-eastern European urban environment

The worldwide restrictions due to the COVID-19 pandemic induced a radical change in urban air quality. The objective of this work was to study and understand the variation of PMcoarse and PM2.5 pollution besides other air quality (AQ) parameters in the city of Debrecen, Hungary from 2018 to 2022. To achieve this goal, we introduced an integrated approach that combines source apportionment by receptor modelling, trajectory-based statistical methods and takes into account local and regional meteorology. Concentration, elemental composition, and sources of atmospheric particulate matter (APM) were determined for four lockdown periods with varying levels of restrictions, two transition intervals and two relaxation periods in 2020-22, and they were compared to corresponding baseline values from 2018-19. The concentration, composition and sources of PM2.5, PMcoarse showed strong seasonality. The main component of PMcoarse was mineral dust (32%), while BC (15%), sulphate aerosols (19%) and mineral dust (13%) were the major constituents of PM2.5. Source apportionment by Positive Matrix Factorization identified 8 sources on both size fractions: two types of soil, traffic, combustions, biomass burning, biogenic emission, sea salt, construction, roadworks, secondary sulphate. The relative contribution of sources did not change during the pandemic compared to the previous two years. However, considering the whole 2-year-long period affected by restrictions, a 20–25% reduction was detected in the concentrations and source contributions, apart from temporary sources like roadworks or sea salt episodes. The changed habits of the population could be traced in the evolution of APM. Before the pandemic, increased concentration of traffic related pollutants was characteristic of the school year, while in 2020-21 it was more typical in the summers, the relaxation periods. On the other hand, staying at home during lockdowns led to a significant increase in biomass burning from domestic heating in spring 2020 and 2021. By studying the individual lockdown and relaxation periods, we have shown that the evolution of APM pollution in relatively short, 2-3-month intervals was significantly influenced by local and regional meteorological parameters, the origin of air masses, desert dust episodes and periodic construction works.

Risk assessment and source apportionment of available atmospheric heavy metal in a typical sandy area reservoir in Inner Mongolia, China

This study evaluated dry and wet deposition of atmospheric heavy metals (HMs) in a sandy area of Inner Mongolia, China, with the Dahekou Reservoir, Xilin Gol League, adopted as the study area. Monthly monitoring of atmospheric HM dry and wet deposition was conducted over one year (2021 to 2022) at 12 monitoring points, producing 144 dry and wet deposition samples, respectively. The sample contents of eight HMs (Cr, Ni, Pb, Cu, Zn, Mn, As, and Cd) were determined to estimate the fluxes of available forms of heavy metal (AHM) in dry and wet deposition. The potential ecological index (Eri), risk assessment coding (RAC), and ratio of secondary phase to primary phase (RSP) were used to evaluate the impact of atmospheric HM dry deposition on ecological security. Correlation analysis, principal component analysis, and the absolute principal component scores-multiple linear regression (APCS-MLR) receptor model were used to quantitatively analyze the sources of AHMs in atmospheric dry and wet deposition. The results showed that the study area experienced annual dry and wet deposition fluxes of AHMs of 1712.59 kg and 534.97 kg, respectively. Atmospheric heavy metal dry deposition over the entire year presented a strong ecological risk, with Cd contributing most to this risk. Risk assessment of HM speciation showed that the greatest risks of migration and transformation were for Cd and Pb. The APCS-MLR receptor model identified five and three sources of dry and wet deposition, respectively, in order of proportion of total contribution of: natural wind and sand > road traffic and coal combustion > mineral mining > other human activities > industrial soot.

A structure-activity relationship modelling of opioid compounds by using machine learning

Opiates are among the oldest drugs that are used to treat many medical problems. They are analgesic and sedative drugs that contain opium. The morphine is its most active ingredient and it is a widely used pain reliever despite its side effects. The main objective of this study is to construct a model which gives the structure-activity relationship among a series of mu-opioid ligands and molecular docking results. For this purpose, a model of mu-opioid receptors using machine learning is introduced. By obtaining a relationship between the docking results and the in vivo test, we could predict the biological effect of the newly synthesized ligands.

Quantitative Analysis of Heavy Metal Pollution Sources, Influencing Factors, and Delineation of Pollution Risk Areas in Industrial Soil: A Combined Application of Receptor Models and GeoDetector

Quantitative Analysis of Heavy Metal Pollution Sources, Influencing Factors, and Delineation of Pollution Risk Areas in Industrial Soil: A Combined Application of Receptor Models and GeoDetector

Residential Wood Combustion in Germany: A Twin-Site Study of Local Village Contributions to Particulate Pollutants and Their Potential Health Effects.

Residential wood combustion contributing to airborne particulate matter (PM10) was studied for 1 year at two sites in the village of Melpitz. Significant excess pollution was observed at the Melpitz center compared to that at the TROPOS research station Melpitz reference site, situated only 700 m away. Local concentration increments at the village site for the combustion PM constituents organic carbon, elemental carbon, levoglucosan, and benzo[a]pyrene were determined under appropriate wind directions, and their winter mean values were 0.7 μg m-3, 0.3 μg m-3, 0.1 μg m-3, and 0.4 ng m-3, representing relative increases over the regional background concentration of 24, 70, 61, and 107%, respectively. Yearly, weekly, and diurnal profiles of village increments suggest residential heating as the dominant source of this excess pollution, mainly originating from wood combustion. Receptor modeling using positive matrix factorization quantified 4.5 μg m-3 wood combustion PM at the village site, representing an increment of 1.9 μg m-3 and an increase of ∼75% over the 2.6 μg m-3 regional background wood combustion PM. This increment varied with season, temperature, and boundary layer height and reached daily mean values of 4-6 μg m-3 during unfavorable meteorological conditions. Potential health effects were estimated and resulted in an all-cause mortality from short-term exposure to wood combustion PM of 2.1 cases per 100,000 inhabitants and year for areas with similar wood smoke levels as observed in Melpitz. The excess cancer risk from the concentrations of polycyclic aromatic hydrocarbons was 6.4 per 100,000. For both health metrics, the very local contributions from the village itself were about 40-50%, indicating a strong potential for mitigation through local-scale policies. A compilation of literature data demonstrates wood combustion to represent a major source of PM pollution in Germany, with average winter-time contributions of 10-20%. The present study quantifies the negative impacts of heating with wood in rural residential areas, where the continuous monitoring of air quality is typically lacking. Further regulation of this PM source is warranted in order to protect human health.

Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

Quantification and Chemical Characterization of Plastic Additives and Small Microplastics (

Road dust is one of the environment's most important microplastic and plastic additive sources. Traffic vehicles and the wear of tires can release these emerging contaminants, which can be resuspended in the air and washed off by stormwater runoff. In this study, a concurrent quantification and chemical characterization of additives, plasticizers, natural and non-plastic synthetic fibers (APFs), and small microplastics (SMPs, <100 µm) in samples of highway road dust (HWRD) was performed. The sampling procedure was optimized, as well as pretreatment (extraction, purification, and filtration) and analysis via micro-FTIR. The average length of the SMPs was 88 µm, while the average width was 50 µm. The highest abundance of SMPs was detected in HWRD 7 (802 ± 39 SMPs/g). Among the polymers characterized and quantified, vinyl ester and polytetrafluoroethylene were predominant. APFs' average particle length was 80 µm and their width was 45 µm, confirming that both of these emerging pollutants are less than 100 µm in size. Their maximum concentration was in RD7, with 1044 ± 45 APFs/g. Lubricants and plasticizers are the two most abundant categories, followed by vulcanizing agents, accelerators, and pre-vulcanizing retarders derived mainly from tires. A potential relationship between APFs and SMPs in the different seasons was observed, as their concentration was lower in summer for both and higher in winter 2022. These results will be significant in investigating the load of these pollutants from highways, which is urgently necessary for more accurate inclusion in emission inventories, receptor modeling, and health protection programs by policymakers, especially in air and water pollution policies, to prevent risks to human health.

The Discovery of Putative Small Molecules via Ligand-based Pharmacophore Modelling Targeting Human Tau Protein for an Effective Treatment of Parkinson’s Disease

Introduction: The human tau protein is a key protein involved in various neurodegenerative disease (NDs) including Parkinson’s disease (PD). The protein has high tendency to aggregate into oligomers, subsequently generating insoluble mass in the brain. Symptoms of PD include tremor, bradykinesia, rigidity, and postural instability. Currently drugs for PD treatment are only symptom-targeted while effective therapeutic treatment remains a challenge. The objective of this study is to identify novel promising anti-PD drugs using computational techniques. Method: ligand-based (LB) receptor modelling was conducted using LigandScout, validated and subjected to Glide XP docking, virtual screening, ADMET, and molecular dynamics predictions. Results: The adopted LB modelling generated pharmacophoric features of 5 hydrogen bond donors, 1 aromatic rings, and 7 hydrogen bond acceptors. The validation result indicated GH score of 0.73 and EF of 36.30 as validation protocols, probing it to be an ideal model. Using 3D query of the modelling a total of 192 compounds were retrieved from interbioscreen database containing 70,436 natural compounds. Interestingly, ligands 1, 2, 3, 4 and 5 orderly indicated higher binding affinities to the receptor with Glide XP docking of -7.451, -7.368, -7.101, -6.878, and -6.789 compared to a clinical drug Anle138b with -4.552 kcal/mol respectively. Furthermore, molecular dynamics and pkCSM pharmacokinetics demonstrated ligands 1, 2, &amp; 4 having better stability and low toxicity profiles compared to the reference. Conclusion: In summary, the study pave way for discovery of small molecules that could be recommended as adjuvant /single candidate as ant-PD candidates upon further translational study.

Origin and transformation of volatile organic compounds at a regional background site in Hong Kong: Varied photochemical processes from different source regions

Volatile organic compounds (VOCs) are important gaseous constituents in the troposphere, impacting local and regional air quality, human health, and climate. Oxidation of VOCs, with the participation of nitrogen oxides (NOx), leads to the formation of tropospheric ozone (O3). Accurately apportioning the emission sources and transformation processes of ambient VOCs, and effectively estimation of OH reactivity and ozone formation potential (OFP) will play an important role in reducing O3 pollution in the atmosphere and improving public health. In this study, field measurements were conducted at a regional background site (Hok Tsui; HT) in Hong Kong from October to November 2020 with proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). VOC data coupled with air mass back trajectory cluster analysis and receptor modelling were applied to reveal the pollution pattern, emission sources and transformation of ambient VOCs at HT in autumn 2020. Seven sources were identified by positive matrix factorization (PMF) analysis, namely vehicular + industrial, solvent usage, primary oxygenated VOCs (OVOCs), secondary OVOCs 1, secondary OVOCs 2 (aged), biogenic emissions, and background + biomass burning. Secondary formation and vehicular + industrial emissions are the vital sources of ambient VOCs at HT supersite, contributing to 20.8 % and 46.7 % of total VOC mixing ratios, respectively. Integrated with backward trajectory analysis and correlations of VOCs with their oxidation products, short-range transport of air masses from inland regions of southeast China brought high levels of total VOCs but longer-range transport of air masses brought more secondary OVOCs in aged air masses. Photolysis of OVOCs was the most important contributor to OH reactivity and OFP, among which aldehyde was the dominant contributor. The results of this study highlight the photochemical processing of VOCs from different source regions which should be considered in strategy making for pollution reduction.

Hydrochemical Characteristics and Control Factors of Surface Water in Kuaize River Basin at the Upper Pearl River

The Kuaize River is a small typical karst watershed in the source area of the Pearl River as well as an important coal mining area in Eastern Yunnan with a fragile ecological environment. Strengthening the research on the water environment in the region plays an important role in supporting the comprehensive management of the ecological environment and water resources in the source region of the Pearl River. Through the systematic collection of surface water, karst groundwater, and mine water samples, mathematical statistics analysis, correlation analysis, ion ratio analysis, absolute principal component scores multiple linear regression(APCS-MLR), and other methods were used to study the characteristics of hydrochemical evolution and control factors in Kuaize River Basin. The results showed that the average pH value of surface water in Kuaize River Basin was 7.8, which was weakly alkaline. The main cations were Ca2+ and Na+, showing the characteristics of Ca2+>Na+>Mg2+>K+. The main anions were HCO3- and SO42-, showing the characteristics of HCO3->SO42->NO3->Cl-. The variation coefficients of Na+, SO42-, and NO3- in surface water were high, showing strong spatial variability. The water chemical type of the trunk stream was mainly HCO3-Ca, whereas the water chemical type of the tributary was relatively complex, mainly HCO3-Ca, HCO3-Ca·Na, and HCO3·SO4-Ca·Na. The chemical composition of surface water was mainly affected by rock weathering, cation exchange, and human activities. Ca2+, Mg2+, Na+, and HCO3- in surface water mainly came from the weathering of carbonate rock and silicate rock; SO42- mainly came from the oxidation of sulfide, such as pyrite in coal seams; K+, Cl-, and NO3- mainly came from domestic sewage and agricultural activities. The APCS-MLR receptor model analysis results showed that the surface water in the Kuaize River Basin was mainly affected by sulfide oxidation, carbonate weathering, weathering of silicate rock in mine water, domestic sewage, agricultural activities, and unknown sources. In general, the contribution rate of human activities such as mining, domestic sewage, and agricultural activities to the surface water reached 47.17%, indicating that human activities were the key driving factor of surface water chemistry in the Kuaize River Basin.