Contribution Of Different Sources Research Articles

Spatial pattern, source apportionment, and source-oriented health risk quantifying of heavy metals in farmland soils of southern China.

The contamination of heavy metal has permeated many parts of China, especially in densely populated and industrialized southern China. This study focused on the degree of pollution in farmland soil heavy metals (HMs), and its spatial distribution characteristics and source apportionment. Meanwhile, we conducted an evaluation of the health risks attributed to soil HMs and analyzed the factors that impact them. We found that the distribution of five heavy metals is mainly concentrated in the east-central and southern parts of the study area. Specifically, Cd and Hg have high levels of pollution and present potential ecological risks. The pollution sources of five HMs were analyzed utilizing positive matrix factorization. The results revealed that the contribution of different sources keeps the following order: natural source (42.42%), agricultural activities (29.93%), industrial pollution source (20.49%), and atmospheric deposition pollution (7.16%). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were relatively high. Children and the elderly are more vulnerable to the negative effects of Cr, As. Using structural equation modeling, we found soil property is a vital factor affecting soil contamination, with the soil organic matter and cation exchange capacity having a relatively greater impact on heavy metals pollution. Our study provides some data reference and guidance for soil ecological protection and restoration.

Capillary drops, capillary pooled, and venous blood samples for determining hemoglobin concentration using HemoCue: A measurement system analysis.

Several external and internal factors can affect the performance and variability of Hemoglobin concentration [Hb] measurements using HemoCue, and documentation on the contribution of different sources of [Hb] variation is limited. We used an experimental repeated measurements design with nine randomly selected participants, three HemoCue devices, and three trained field workers. HemoCue measurements for all samples were repeated three times. The [Hb] measurement system considers four sources of error: 1) HemoCue devices, 2) field workers, 3) between individuals, and 4) within individuals. A concordance analysis was used to assess accuracy and precision, and a linear mixed model was used to estimate mean differences (bias) from blood specimens, anticoagulants, and to estimate the contribution of the 4 sources of error to [Hb] measurements. Positive mean [Hb] differences were found: 1.34 g/dL for capillary drops, 0.81 g/dL for pooled capillary blood samples, 0.756 g/dL for venous blood stored with EDTA, and 0.911 g/dL for venous blood stored with heparin. The mean [Hb] difference for venous blood with EDTA was used as a correction factor for all results measured using a HemoCue. After adjustment, capillary drops showed a mean difference of 0.585 g/dL, and pooled capillary samples were not significantly different. The individual variability was 95.8% of total variance, HemoCue devices contributed 2.1% of measurement error, field staff contributed 0.4%, and the residual was 1.7%. The HemoCue [Hb] measurement system is reliable in controlled environments, with a small measurement error of 4.2%.

Abatement of ionizing radiation for superconducting quantum devices

Ionizing radiation has been shown to reduce the performance of superconducting quantum circuits. In this report, we evaluate the expected contributions of different sources of ambient radioactivity for typical superconducting qubit experiment platforms. Our assessment of radioactivity inside a typical cryostat highlights the importance of selecting appropriate materials for the experiment components nearest to qubit devices, such as packaging and electrical interconnects. We present a shallow underground facility (30-meter water equivalent) to reduce the flux of cosmic rays and a lead shielded cryostat to abate the naturally occurring radiogenic gamma-ray flux in the laboratory environment. We predict that superconducting qubit devices operated in this facility could experience a reduced rate of correlated multi-qubit errors by a factor of approximately 20 relative to the rate in a typical above-ground, unshielded facility. Finally, we outline overall design improvements that would be required to further reduce the residual ionizing radiation rate, down to the limit of current generation direct detection dark matter experiments.

The impact of volcanic eruptions, pyrocumulonimbus plumes, and the Arctic polar vortex intrusions on aerosol loading over Tomsk (Western Siberia, Russia) as observed by lidar from 2018 to 2022

ABSTRACT Long-term ground-based lidar observations are important in estimating the contribution of different sources (mostly volcanic eruptions and wildfires) to aerosol loading of the atmosphere and in developing and improving various climate models. In this paper, we carefully analyse aerosol scattering ratio profiles and aerosol layers, observed with 532-nm lidar measurements at the Siberian Lidar Station in Tomsk (56.48°N, 85.05°E) over the period 2018–2022, and identify the layers’ potential sources. To compare aerosol loading over Tomsk for the last five years (2018–2022) with that for previous years, we also present and discuss time series of integrated aerosol backscatter coefficient (IABC) for three altitude ranges. The first range (15–30 km) reveals the pure stratospheric aerosol loading provided by volcanic eruptions, the second range (11–15 km) is responsible mainly for aftereffects of wildfire smoke plumes, and the third one (11–30 km) demonstrates aerosol contribution from both sources. In particular, we found for the period 2001–2022 that the annual average IABC reached its maximal values of 2.91 × 10−4 sr−1 (15–30 km) and 8.21 × 10−4 sr−1 (11–30 km) in 2022 due to the Hunga Tonga-Hunga Ha’apai volcanic eruption. The period 2020–2022 exhibited a significant increase in the relative aerosol content at altitudes of 11–15 km in the total aerosol loading at 11–30 km over Tomsk from 54.4% in 2020 to 64.8% in 2022, which indicates a shift of the main part of aerosol loading to altitudes of 11–15 km due to the increased wildfire activity in North America and north-eastern Asia. The Arctic polar vortex is also revealed to distort the real aerosol vertical distribution over Tomsk by replacing it with the aerosol distribution inside the vortex on measurement days when the vortex is over the lidar site.

Study on main sources of aerosol pH and new methods for additional reduction of PM2.5 during winter severe pollution: Based on the PMF-GAS model

In recent years, the air quality in urban areas of China has been a significant improvement. However, further advancements in air quality remain challenging, particularly in addressing the persistent problem of severe particulate matter (PM) pollution in winter. In-depth research on the aerosol acidity (pH) is beneficial for analyzing the pollution characteristics of PM and providing support for the continuous improvement of PM pollution. In this study, the sources of water-soluble inorganic ions (WSIIs) of PM2.5 in Zhengzhou were analyzed based on the PMF-GAS method, and the contribution of different sources to aerosol acidity was further investigated in conjunction with the ISORROPIA-II thermodynamic model. The research results indicate that secondary aerosols are the main source, and controlling the emissions of secondary aerosols and gaseous precursors is a crucial measure for reducing PM2.5 concentrations. Furthermore, the study revealed that targeted reductions of major pollution sources across varying pH ranges could significantly lower PM2.5 concentrations. In the range of pH < 4, when the contribution of secondary aerosol source is 23.9%, PM2.5 concentrations can be reduced by 40.6%, resulting in an additional emission reduction of 5.9 μg m−3. In the range of 4 < pH < 6, PM2.5 can be reduced by 36.9% at a contribution of 16.5% from secondary aerosol source, achieving an additional emission reduction of 7.0 μg m−3. This research provides important theoretical support for gaining a deeper understanding of the sources and control of atmospheric pollution, offering strategies to expedite the achievement of PM2.5 standards and further mitigating its impacts on human health and the environment.

Chemical composition of cloud and rainwater at a high-altitude mountain site in western India: source apportionment and potential factors.

This study focuses on the chemical composition of cloud water (CW) and rainwater (RW) collected at Sinhagad, a high-altitude station (1450m AMSL) located in the western region of India. The samples were collected during the monsoon over two years (2016-2017). The chemical analysis suggests that the concentration of total ionic constituents was three times higher in CW than in RW, except for NH4+ (1.0) and HCO3- (0.6). Compared to RW, high concentrations of SO42- and NO3- were observed in CW. The weighted average RW pH (6.5 ± 0.3) was slightly more alkaline than CW pH (6.1 ± 0.5). This can be attributed to the high concentrations of neutralizing ions such as nss-Ca2+, nss-Mg2+, K+, and NH4+, indicating the greater extent of wet scavenging during rainfall. These ions counteract the acidity generated by SO42- and NO3-. A high correlation between Ca2+, Na+, K+, NO3-, and SO42- makes it difficult to estimate the contribution of SO42- from different sources. Anthropogenic sulfur emissions and soil dust significantly influence the ionic composition of clouds and rain. Positive matrix factorization (PMF) was used to identify the contribution of different sources to the samples. In the CW, the extracted factors were cooking and vehicles, aging sea salt, agriculture, and dust. In RW, the factors were industries, cooking and vehicles, agriculture and dust, and aging sea salt. The findings of this study have significant implications for the monsoon build-up, ecosystems, agriculture, and climate change.

It's time to reevaluate the list of priority polycyclic aromatic compounds: Evidence from a large urban shallow lake

Monitoring only 16 priority PAHs (Pri-PAHs) may greatly underestimate the pollutant load and toxicity of polycyclic aromatic compounds (PACs) in aquatic environments. There is an urgent need to reevaluate the list of priority PACs. To determine which PACs deserve priority monitoring, the occurrence, sources, and toxicity of 78 PACs, including 24 parent PAHs (Par-PAHs), 49 alkylated PAHs (Alk-PAHs), 3 oxygenated PAHs (OPAHs), carbazole, and dibenzothiophene were investigated for the first time in Lake Chaohu sediments, China. Concentrations of ∑Par-PAHs, ∑Alk-PAHs, and ∑OPAHs ranged from 35 to 165, 3.4–26, and 7.7–26 ng g−1, respectively. Concentrations of 16 Pri-PAHs have decreased by 1–2 orders of magnitude compared to a decade ago, owing to the effective implementation of PAHs emission control measures. Comparisons with the sediment quality guidelines indicated that 16 Pri-PAHs have negligible adverse effects on benthic organisms. Positive matrix factorization (PMF) model results showed that coal combustion was the major source of PACs (accounting for 23.5 %), followed by traffic emissions (23.4 %), petroleum volatilization (21.9 %), wood/biomass combustion (18.2 %), and biological/microbial transformation (13.1 %). The toxicity of PACs was assessed by calculating the BaP toxic equivalent concentrations (TEQBaP) and toxic units. It was found that Par-PAHs were the predominant toxic substances. In addition, monomethyl-BaPs, OPAHs, BeP, and 7,12-DMBaA should be prioritized for monitoring due to their noticeable contributions to overall toxicity. The contributions of different sources to the toxicity of PACs were determined based on PMF model results and TEQBaP values, which revealed that combustion sources mainly contributed to the comprehensive toxicity of PACs in Lake Chaohu sediments.

Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy.

Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.

Application of VIC-WUR model for assessing the spatiotemporal distribution of water availability in anthropogenically-impacted basins

Climate change, rapid increase in population, and urbanization have given rise to over-exploitation and shortage of freshwater resources. Acquiring accurate representations of fresh water availability is therefore crucial to sustainable development. Based on the underlying hydrological processes and storage types, fresh water can be divided into blue and green water components. Hence, quantification of the spatio-temporal distribution of blue and green water requires estimating the components of terrestrial water cycle. In this study, a framework is proposed to evaluate the water scarcity using the blue and green water concept. In this framework, the Wageningen Institute and Research (WUR) version of the Variable Infiltration Capacity Model (VIC) is employed to simulate terrestrial water cycle. Compared to VIC model, VIC-WUR model employs additional modules to account for sectoral water demands, return flows, and groundwater withdrawals, however, very limited number of its applications have been reported so far. The Divandareh-Bijar (DBJ) basin in west of Iran is selected because it has experienced severe anthropogenic alterations in the past three decades. To address precipitation and parameter uncertainty, the VIC-WUR model is separately calibrated for three precipitation datasets. As a part of this framework, the calibrated model is used to simulate blue and green water components and their corresponding scarcity metrics for the first time. According to the results, blue and green water resources in the DBJ are relatively scarce. The results also highlight the importance of considering anthropogenic impacts on the spatiotemporal dynamics of green and blue water resources. Additionally, quantification of the uncertainty in blue and green water components can help produce more reliable estimations for intelligent management of water resources. The ANalysis Of VAriance (ANOVA) method is therefore used to quantify the contribution of three major factors, including precipitation inputs, model parameters, and the interaction of these two, on the uncertainty of calculated water scarcity metrics. Structural uncertainty is implicitly addressed through the use of the modified version of the VIC model. The results indicate seasonality of these contributions for the two metrics under investigation and the differences in relative contribution of different sources to the overall uncertainty. The proposed framework could be employed to identify water stress in various spatiotemporal scales across the globe.

Source Apportionment of Morphine in Wastewater

It is a new approach to identify legal or illegal use of morphine through information on municipal wastewater. However, the sources of morphine in wastewater are complex, and distinguishing the contribution of different sources has become a key issue. A total of 262 influent samples from 61 representative wastewater treatment plants in a typical city were collected from October 2022 to March 2023. The concentrations of morphine, codeine, thebaine, papaverine, noscapine, and monoacetylmorphine were analyzed in wastewater and poppy straws. Combined with the proportion of alkaloids in poppy straws, the source analysis of alkaloids in wastewater was analyzed using the ratio method and positive matrix factorization model (PMF). Only five alkaloids were detected in wastewater, and monoacetylmorphine, a metabolite of heroin, was not detected. The concentrations of morphine and codeine were significantly higher than those of noscapine, papaverine, and thebaine. By constructing the ratios of codeine/(morphine + codeine) and noscapine/(noscapine + codeine), the source of poppy straw could be qualitatively distinguished. The PMF results showed that three sources of morphine for medical use, poppy straw, and codeine contributed 44.9%, 43.7%, and 9.4%, respectively. The different sources varied in these months due to the COVID-19 and influenza A outbreaks, in which the use of drugs containing poppy straws and codeine was the main source, whereas the use of morphine analgesics remained relatively stable. Inventory analysis further demonstrated the reliability of the source contributions from the PMF model, and morphine was not abused in this city.

Development of a multi-level factorial data assimilation approach for groundwater contaminant sources identification

The ensemble Kalman filter (EnKF) is recognized as a widely used approach for groundwater contamination sources (GCSs) parameters identification. The identification accuracy is affected by the quantification of sources of uncertainty (such as random disturbances on model inputs and measurements) in the assimilation process, but few studies have revealed the contributions of different sources of uncertainty on identification accuracy. To this end, this study developed an innovative multi-level factorial data assimilation (MFDA) approach, which integrates ensemble Kalman filtering and multi-level factorial analysis, to comprehensively reveal the impact of sources of uncertainty on the GCSs parameters identification accuracy for the first time. Not only did it explore the direct impact of individual sources of uncertainty on the GCSs parameters identification accuracy, more importantly, it also revealed the composite ones from multi-parameter interactions among these sources of uncertainty. The developed MFDA approach quantified the contributions of multiple sources of uncertainty, including model input data error (i.e. precipitation), model parameter error (i.e. hydraulic conductivity), contaminant concentration measurements error, and ensemble size. And the feasibility of the developed approach was verified by two hypothetical numerical case studies. The results indicated that: i) it was critically important to reduce the contamination concentration measurement errors to improve GCSs parameter identification accuracy. ii) model parameter and model input data were not sensitive to the identification accuracy. iii) the interaction between sources of uncertainty significantly affects the identification accuracy, which illustrated the interaction cannot be ignored in the identification process. Overall, this study highlights that the developed MFDA approach represents a promising solution for improving GCSs parameters identification accuracy.

Who matters and why? The contributions of different sources of social support to doctoral students' academic engagement

AbstractSocial support is a crucial factor in the academic engagement of doctoral students, which is vital to their overall success. While past studies have mostly focused on the support from doctoral supervisors, support from other significant groups, including institutions, peers and families, has been largely neglected, and even no study has investigated their contributions to doctoral students' development. Drawing from the job demands‐resources model, this study investigated the contributions of different sources (institutions, supervisors, peers and families) of social support to doctoral students' academic engagement. It further examined the mediating role of grit within these relationships. A sample of 472 doctoral students across various disciplines from 10 universities in mainland China responded to an online survey. Results showed that institutional support and supervisory support positively predicted doctoral students' academic engagement, while peer support and family support did not significantly predict academic engagement. Furthermore, grit was found to mediate the relationship between support from institutions and supervisors and academic engagement. Practical implications for higher education institutions, doctoral supervisors and programs are discussed.

Job function as determinant of clinker exposure at workplaces during cement production.

In the cement production industry, exposure to airborne particulate matter is associated with a decline in lung function and increased airway symptoms. Exposure to clinker-the major constituent of cement and supposedly the cause of the observed adverse health effects-was determined recently in 15 cement production plants located in 8 different countries (Estonia, Greece, Italy, Norway, Sweden, Switzerland, Spain, Turkey). It was shown that the median clinker abundance in the thoracic fraction varied between approximately 20% and 70% for individual plants. The present study complements the previous work by investigating the significance of job function as a determinant of clinker exposure. The elemental composition (water and acid-soluble fractions separately) of 1,227 personal thoracic workplace samples was analyzed by positive matrix factorization (PMF) to determine the contribution of different sources to the composition of airborne particulate matter and to quantify the clinker content. Median thoracic mass air concentrations varied for individual job functions between 0.094 and 12 mg/m3 (estimated separately for different plants). The PMF 5-factor solution yielded median relative clinker abundances in the personal thoracic samples between 7.6% and 81% for individual job functions. Thoracic clinker air concentrations are highest for cleaning, production, and maintenance work, and lowest for administration and other work. Foremen and laboratory personnel show intermediate exposure levels. The plant was found to have a much higher contribution to the total variance of the thoracic clinker air concentrations than the job function. Thoracic clinker air concentrations (medians between 0.01 and 5.5 mg/m3) are strongly correlated with the thoracic mass air concentrations and to a lesser extent with the relative clinker abundance in an aerosol sample. Job function is an important predictor of exposure to clinker in the cement production industry. As clinker is suspected to be the causal agent for the observed adverse health effects among cement production workers, the clinker air concentration may be a better exposure metric than thoracic air mass concentration despite the strong correlation between the two. Reduction strategies should focus on the most exposed job categories cleaning, production, and maintenance work.

Hydrochemical Characteristics and Control Factors of Shallow Groundwater in Anqing Section of the Yangtze River Basin

Shallow groundwater is the main source of water for living and industrial and agricultural production in Anqing City, which is an important basic guarantee to maintain the sustainable development of the social economy and regional ecological environment. In order to further study the water chemical characteristics and controlling factors of shallow groundwater in Anqing City, 196 groups of shallow groundwater samples were collected. A Piper diagram graph, Gibbs chart, ion ratio, and mathematical statistics were comprehensively used to study the water chemical characteristics and controlling factors of groundwater in Anqing City, and the contribution of different sources to the water chemical components of groundwater was quantitatively evaluated. The results showed that the shallow groundwater in Anqing City was weakly alkaline, with pH values ranging from 5.84 to 8.38, with an average value of 7.21. The TDS ranged from 47 to 1 620 mg·L-1, with an average of 324.21 mg·L-1. HCO3- and Ca2+ were the main anions, and the water chemical type was HCO3-Ca type. The chemical components of groundwater were affected by rock weathering leaching, cation alternating adsorption, mineral dissolution and precipitation, and human activities. Ca2+, Mg2+, and HCO3- were mainly derived from the weathering dissolution of carbonate and silicate; Na+, Cl-, and SO42- were affected by industrial activities and domestic sewage discharge; and K+ and NO3- were affected by agricultural activities. The APCS-MLR receptor model analysis further revealed that the chemical components of groundwater were mainly geological factors, industrial factors, agricultural factors, and unknown sources, and their contribution rates were 45.35%, 14.19%, 25.38%, and 15.08%, respectively. Geological factors were important sources of hydrochemical components of shallow groundwater, and human activities aggravated the evolution of groundwater hydrochemistry.

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Air pollution biomonitoring in an urban-industrial setting (Taranto, Italy) using Mediterranean plant species

This study presents the first report on the elemental composition of five Mediterranean plant species (Pinus pinaster, Eucalyptus camaldulensis, Nerium oleander, Olea europaea and Pittosporum heterophyllum) to trace industrial emissions in Taranto, Italy, a mixed-use industrial and urban setting. Potential metal sources include vehicular traffic, steel and cement plants, and a petrochemical refinery. Samples were collected from 29 sites covering the Tamburi-Lido Azzurro neighbourhood and the historical quarter Città Vecchia-Borgo. High concentrations of toxic metals were observed in all samples, with marked inter-species variability. Model based clustering identified two distinct groups, one dominated by pine needles with higher metal concentrations than the other group composed of the other four plant species. The contamination factor (CF) and pollution load index (PLI) indices which use background samples to standardise the level of pollution, were used to remove species effect allowing for direct site comparison. Spatial analysis of CF and PLI data identified pollution hotspots near industrial areas and major roads, with areas of little to no air pollution near green spaces. Statistical analysis of the CFs revealed the contribution of different sources to element emissions. Ni and Cr were primarily emitted from the steel plant and petrochemical refinery, while Fe and Al were associated with road traffic emissions, and geogenic elements Ca, Mg, K, and Na were linked to marine spray and Saharan dust. This study demonstrates that combining multiple plant species with pollution indices can be a cost-effective biomonitoring approach for assessing air pollution and creating a high-density spatial monitoring network.

Air pollutant emissions and sources in Lao People’s Democratic Republic: a provincial scale analysis for years 2013-2019

Recent rapid economic development in Lao People’s Democratic Republic (PDR) has increased national fuel consumption, vehicle fleet, industrial output, waste generation, and agricultural production. This has contributed to national average ambient fine particulate matter (PM2.5) air pollutant levels that are four times higher than World Health Organisation guidelines. Emission inventories are a key tool in understanding the major sources to these air pollution levels, and provide a starting point to identify where mitigation action can be targeted. A national air pollutant emission inventory has not been developed in Lao PDR and, combined with a limited air quality monitoring network means there is limited capacity to develop and track the effectiveness of mitigation actions. This study describes the first air pollutant emission inventory at the national and provincial scale for Lao PDR, covering 2013–2019. Emissions of nine air pollutants, and two greenhouse gases, were quantified using national statistics and international default emission factors. In 2019, national total PM2.5, Nitrogen Oxides (NOx), Black Carbon (BC), Sulphur Dioxide (SO2), Non-Methane Volatile Organic Carbons (NMVOCs), and Ammonia (NH3) were 125, 83, 9.7, 26, 219, and 99 thousand tonnes respectively. Key source sectors include forest fires, residential cooking, agriculture, electricity generation, and transport. However, the contribution of different sources varies across provinces. Forest fires are the primary source determining the spatial trend of particulate air pollution while residential and agricultural emissions contribute more significantly to rural provinces such as Savannakhet. Key sectors in major urban provinces (Vientiane Capital and Xayaboury) are industry, transport and electricity generation. These sectors are also significant sources of greenhouse gases (CO2 and CH4), demonstrating the potential for identification, evaluation and prioritisation of actions that simultaneously improve air quality and achieve Lao PDR’s international climate change commitments.

Implementation of an On-Line Reactive Source Apportionment (ORSA) Algorithm in the FARM Chemical-Transport Model and Application over Multiple Domains in Italy

A source apportionment scheme based on gas and aerosol phase reactive tracers has been implemented in the chemical-transport model FARM, to efficiently estimate contributions of different sources to ambient concentrations. The on-line scheme deals with all the main processes that the chemical species undergo in the model, to enhance consistency with the calculation of bulk concentrations. The fate of precursors through gas-phase chemical reactions is followed by an efficient solver that determines their incremental reactivity, while the contributions to the secondary particulate species from their gaseous precursors is determined by assuming the thermodynamic equilibrium between the two phases. The paper details the new employed methodologies and illustrates the application of the apportionment scheme (based on 6 source sectors) to PM10 and O3, simulated on three domains of different dimensions in Italy, all sharing the same horizontal resolution and a common region (Lombardy). Spatial patterns of results show, on average, a relevant contribution of heating on PM10 concentration in January, with local hotspots dominated by road traffic. Contributions appear consistent in the three simulated domains, apart from the boundary conditions, influenced by the dimension of the domain. Hourly series of contributions to O3 concentrations in July at three selected sites show the dominance of boundary conditions, underlining the large scale of O3 formation. Finally, for PM10 components, the resulting sectorial contributions are compared with the impacts computed via the brute force method, showing that results are similar for elemental carbon and sulfate, while they are different for nitrate and ammonium, due to a different allocation of contributions and impacts between the methods. Each approach responds in principle to a different purpose, and their combined use provides possibly a wide set of information useful for addressing the different air quality management needs.

Identification of Causes of Air Pollution in a Specific Industrial Part of the Czech City of Ostrava in Central Europe

This contribution deals with the assessment of air pollution caused by atmospheric aerosol particulate matter fraction PM10 and benzo[a]pyrene (BaP) in the urban agglomeration of Ostrava, located in the Czech Republic in Central Europe. The motivation for this research was the need to identify the sources of air pollution in the area, particularly in locations where the contribution of different sources to concentrations of pollutants of concern has not been elucidated yet. In this study, source apportionment in the vicinity of the industrial hot spot was performed by statistically evaluating measured pollutant concentrations as a function of meteorological variables and using the Positive Matrix Factorization (PMF) receptor model. A significant methodological innovation and improvement over previous assessments was the higher monitoring resolution of benzo[a]pyrene, with samples collected at three-hour intervals instead of the standard 24 h collection period. The key findings indicate that in the cold part of the year, secondary particles—specifically sulfates and ammonium nitrates—were responsible for the most significant portion of PM10 air pollution throughout the area of interest. The contribution of these particles ranged from one-third to two-fifths of the total concentration, except at the industrial site of Ostrava–Radvanice (TORE), where they accounted for approximately one-fifth of the measured pollution concentration level. Emissions from individual household heating were identified as the main source of this type of pollution. With regards to benzo[a]pyrene air pollution, this study found that in the whole area of interest, except for the Ostrava–Radvanice site, it mainly originated from individual heating with coal (90%). In contrast, at the Ostrava–Radvanice site, two-thirds of the benzo[a]pyrene pollution came from the premises of Liberty Ostrava a.s., primarily from coke production, and less than one-third came from local domestic heating. This study also determined the spatial extent of the occurrence of extremely high benzo[a]pyrene concentrations (above 5 ng/m3), which are estimated to affect nearly 10,000 inhabitants. The results confirm that the data from the TORE station are only representative of its immediate surroundings and are not applicable to the assessment of air quality and causes of air pollution in the whole city of Ostrava or the urban area of Ostrava–Radvanice as a whole. After years of research, these findings provide the Ministry of the Environment of the Czech Republic and the government with an accurate basis for implementing measures to address the identified pollution sources in the area of interest. The success of this study was made possible by the adoption of a more detailed sampling approach, which involved a resolution of 3 h instead of 24 h. This methodological improvement is a significant finding and will be useful for future source apportionment studies.

Significant contribution of different sources of particulate organic matter to the photoaging of microplastics

Particulate organic matter (POM), as an important component of organic matter, can act as a redox mediator and thus intervene in the environmental behavior of microplastics (MPs). However, quantitative information on the role of POM in the photoaging of MPs under ultraviolet (UV) light is still lacking. To raise the knowledge gap, through environmental simulation experiments and qualitative/quantitative experiments of active substances, we found that POM from peat soil has stronger oxidation capacity than POM from sediment, and the involvement of POM at high water content makes the aging of MPs more obvious. This is because the persistent radicals and electron-absorbing groups on the surface of POM indirectly generate reactive oxygen species (ROS) by promoting electron transfer, and the dissolved organic matter (DOM) released from POM under UV light (POM-DOM) is further excited to generate triplet-state photochemistry of DOM (3DOM*) to promote the aging of MPs. Theoretical calculations revealed that the benzene ring, mainly C = C, and C = O in the main chain in the plastic macromolecule structure are more susceptible to ROS attack, and the differences in the vulnerable sites contained in different plastic structures as well as the differences in the energy band gaps lead to differences in their aging processes. This study firstly elucidates the key role and intrinsic mechanism of POM in the photoaging of MPs, providing a theoretical basis for a comprehensive assessment of the effect of POM on MPs in the environment.