The presence of heavy metals and metalloids (metal(loid)s) in the food chain is a global problem, and thus, metal(loid)s are considered to be Potentially Toxic Elements (PTEs). Arsenic (As), lead (Pb), mercury (Hg), and cadmium (Cd) are identified as prominent hazards related to human health risks throughout the food chain. This study aimed to carry out a source attribution for metal(loid)s in shallow topsoil of north-midlands, northwest, and border counties of the Republic of Ireland, followed by an assessment of the potential ecological and human health risks. The positive Matrix Factorization (PMF) was used for source characterization of PTEs, followed by the Monte Carlo simulation method, used for a probabilistic model to evaluate potential human health risks. The mean concentrations of prioritized metal(loid)s in the topsoil range in the order of Pb (28.83 mg kg-1) > As (7.81 mg kg-1) > Cd (0.51 mg kg-1) > Hg (0.11 mg kg-1) based on the open-source Tellus dataset. This research identified three primary sources of metal(loid) pollution: geogenic sources (36 %), mixed sources of historical mining and natural origin (33 %), and anthropogenic activities (31 %). The ecological risk assessment showed that Ireland\'s soil exhibits low-moderate pollution levels however, concerns remain for Cd and As levels. All metal(loid)s except Cd showed acceptable non-carcinogenic risk, while Cd and As accounted for high to moderate potential cancer risks. Potato consumption (if grown on land with elevated metal(loid) levels), Cd concentration in soil, and bioaccumulation factor of Cd in potatoes were the three most sensitive parameters. In conclusion, metal(loid)s in Ireland present low to moderate ecological and human health risks. It underscores the need for policies and remedial strategies to monitor metal(loid) levels in agricultural soil regularly and the production of crops with low bioaccumulation in regions with elevated metal(loid) levels.

In recent years, the coastal area in East China has experienced elevated volatile organic compounds (VOCs) levels during specific periods. VOCs have become one of the major atmospheric pollutants in these areas. In this study, 64 compounds including alkanes, alkenes, halohydrocarbons, aromatics, and oxygenated VOCs (OVOCs) were obtained by the TO-15 method through a 12-month campaign in industrial, urban and suburban areas in the Yangtze River Delta of China. The overall trends of total VOC (TVOC) concentrations at eight sampling sites were as follows: winter > autumn > spring > summer. The proportion of VOC categories was various at industrial sites, while OVOCs and halohydrocarbons had high proportions at urban sites and suburban sites, respectively. Coating, vehicle emission, petrochemical source, industrial source, and gasoline volatilization were identified as the major VOC emission sources by the positive matrix factorization model. Petrochemical and coating sources were the prime VOC sources at industrial sites. Aromatics contributed the most ozone formation potential at industrial sites, while OVOCs provided the main contributions at both urban and suburban sites during four seasons. According to the health risk assessment, a high probability of non-carcinogenic risk existed at three industrial sites. Special attention should be given to certain VOCs, such as acrolein and 1,2-dibromoethane in industrial areas.

Volatile organic compounds (VOC) are considered a class of pollutants with a significant presence in indoor and outdoor air and serious health effects. The aim of this study was to measure and evaluate the levels of outdoor and indoor VOCs at selected sites on Rhodes Island, Greece, during the cold and warm periods of 2023. Spatial and seasonal variations were evaluated; moreover, cancer and non-cancer inhalation risks were assessed. For this purpose, simultaneous indoor-outdoor air sampling was carried out on the island of Rhodes. VOCs were determined by Thermal Desorption-Gas Chromatography/Mass Spectroscopy (TD-GC/MS). Fifty-six VOCs with frequencies ≥ 50% were further considered. VOC concentrations (∑56VOCs) at all sites were found to be higher in the warm period. In the warm and cold sampling periods, the highest concentrations were found at the port of Rhodes City, while total VOC concentrations were dominated by alkanes. The Positive Matrix Factorization (PMF) model was applied to identify the VOC emission sources. Non-cancer and cancer risks for adults were within the safe levels.

To enhance stakeholder engagement and foster the inclusion of interests of citizens in radiation protection research, a comprehensive online survey was developed within the framework of the European Partnership PIANOFORTE. This survey was performed in 2022 and presented an opportunity for a wide range of stakeholders to voice their opinions on research priorities in radiation protection for the foreseeable future. Simultaneously, it delved into pertinent issues surrounding general radiation protection. The PIANOFORTE e-survey was conducted in the English language, accommodating a diverse range of participants. Overall, 440 respondents provided their insights and feedback, representing a broad geographical reach encompassing 29 European countries, as well as Canada, China, Colombia, India, and the United States. To assess the outcomes, the Positive Matrix Factorization numerical model was applied, in addition to qualitative and quantitative assessment of individual responses, enabling the discernment of four distinct stakeholder groups with varying attitudes. While the questionnaire may not fully represent all stakeholders due to the limited respondent pool, it is noteworthy that approximately 70% of the participants were newcomers to comparable surveys, demonstrating a proactive attitude, a strong willingness to collaborate and the necessity to continuously engage with stakeholder groups. Among the individual respondents, distinct opinions emerged particularly regarding health effects of radiation exposure, medical use of radiation, radiation protection of workers and the public, as well as emergency and recovery preparedness and response. In cluster analysis, none of the identified groups had clear preferences concerning the prioritization of future radiation protection research topics.

To investigate the concentration characteristics and sources of metal elements in PM2.5 during winter heavy pollution in the southern Sichuan urban agglomeration (Zigong, Luzhou, Neijiang, and Yibin), the metal elements in PM2.5 were measured using membrane sampling methods from December 30, 2018 to January 14, 2019, and the enrichment factor method (EF) and positive matrix factorization(PMF) were applied to investigate the sources of metal elements. The metal element observation data of Zigong in the same period of 2015 were also used to investigate the changes in metal element pollution and enrichment in Zigong in the middle and end of the implementation of China\'s Air Pollution Prevention and Control Action Plan. The main findings were as follows:① The concentrations and percentages of metal elements in particulate matter in different cities did not differ significantly. The elements with higher concentrations in the four cities showed similarities, with Al, Sb, and Fe at the top. From the comparison of different observation periods in Zigong, the concentrations of all elements except Tl changed. ② The results of the enrichment factor calculation showed that the enrichment of the elements Cr (Zigong and Yibin), Ni, Cu, As, Se, Ag, Cd, Sb, Tl, and Pb in the urban agglomeration was high. The comparison of the enrichment levels of elements in Zigong for different observation periods showed that the enrichment levels of all elements, except Cu, tended to decrease in the winter observation period of 2018. ③ The results of PMF source analysis showed that the metal elements in each city mainly originated from dust sources, coal-fired sources, industrial sources, and traffic sources, whereas there was a mixed contribution among the sources. The contribution of the main sources differed among cities, in which Zigong was dominated by traffic dust sources and mixed sources, Luzhou was dominated by industrial sources, Neijiang had a similar contribution from different sources, and Yibin was dominated by traffic sources.

Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM10 sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM10 filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM10 were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM10, OPAA and OPDDT, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM10 mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM10 and OP in complex environments and are thus useful for PM control strategies and actions.

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

The concentration of 56 volatile organic compounds (VOCs) in the ambient air of Shenyang was continuously monitored at four sites in 2021. The characteristics, sources, secondary pollution potential and health risks of VOCs in different functional regions of Shenyang were discussed. The results indicate that the concentration of VOCs in industrial regions was significantly higher than that in non-industrial regions, with a mean of 41.09 ± 69.82 parts per billion volumes (ppbv) compared to 19.99 ± 17.86 ppbv (commercial & residential region in urban fringe), 27.51 ± 28.81 ppbv (educational & scenic region) and 29.71 ± 23.97 ppbv (commercial & residential region in urban center). The positive matrix factorization (PMF) model was utilized to assign the sources of VOCs in Shenyang, and six factors were recognized: gasoline vehicles (34.8 %), diesel vehicles (28.3 %), combustion (11.4 %), biogenic emissions (9.7 %), industrial processes (8.2 %), and fuel evaporation (7.7 %). The results of the reactivity evaluation indicated that the ozone (O3) formation potential (OFP) was primarily influenced by industrial processes (29.2 %), diesel vehicles (25.7 %), biogenic emissions (17.0 %). These three factors were also the top three contributors to secondary organic aerosol formation potential (SOAP), accounting for 44.2 %, 9.4 % and 30.3 %, respectively. At the all four sites, the non-carcinogenic and carcinogenic risks of VOCs ranged from 1.6 × 10-2 to 3.8 × 10-2 and from 2.3 × 10-6 to 3.3 × 10-6, respectively. And the main risks can be attributed to emissions from industrial processes and gasoline vehicles. These findings suggested to strengthen the control of vehicle emissions throughout all regions in Shenyang and industrial processes emissions in industrial regions.

This study assessed the air quality status in different functional zones of Dhanbad-a coal-mining and industrial hub, based on the measurement of aromatic and halogenated volatile organic compounds (VOCs) using gas chromatography. The study encompasses source apportionment of VOCs and their chemical reactivity in terms of OH radical loss rate (LOH), ozone-forming potential (OFP), and their secondary organic aerosol forming potential (SOAp). Furthermore, prioritization of VOCs based on a fuzzy-analytical hierarchical process (F-AHP) has also been done. The results found xylene species to have the highest concentration in all three seasons across traffic-intersection and industrial zones and toluene at the institutional zone. The study identified four sources using positive matrix factorization (PMF) model, viz., mixed traffic exhaust (35%), coal combustion sources (30%), industrial (26%), and solvent usage (9%). LOH and SOAp were ~ 16 times more at the industrial and traffic-intersection zone than the institutional zone. The aromatic species contributed 97% to the OFP, and many species exhibited less contribution to the mixing ratio of VOCs but displayed a high contribution to LOH, OFP, and SOAp, suggesting the need to prefer reactivity-based strategies in addition to concentration-based strategies in the future for their regulation. The F-AHP-based priority component analysis identified 16 species out of 29 in the priority watch list (nine in tier-1, four in tier-2, and three in tier-3). The paucity of data and lack of ambient air quality standards on VOCs (except benzene) make it difficult to determine which aspect should be dealt with first and which species require more attention. Therefore, the F-AHP method used in this study could help identify the influencing parameters to be considered while devising efficient VOC management policies.

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) are associated with adverse health effects and show spatial variation in three dimensions. The present study attempted to evaluate source contributions of PM2.5 and toxic VOCs in a metropolitan area focusing on the associated vertical variations. A special emphasis is put on the effects of the elevated expressway on the vertical variability of contribution estimates of the identified sources. Nine source factors, i.e., soil dust, sea salt/oil combustion, secondary nitrate, industrial emission, aged VOCs/secondary aerosol, traffic-related I, solvent use/industrial process, secondary sulfate, and traffic-related II, were identified using positive matrix factorization (PMF). The main contributors to PM2.5 were secondary sulfate (19.1%) and traffic-related emissions (traffic-related I and II, 16.1%), whereas the largest contributors to VOCs were traffic-related emissions (37.6%). The influence of the elevated expressway is suggested to be particularly critical on vertical variations of traffic-related emissions, including aging and secondary formation of locally accumulated air pollutants near roads. Increasing the building porosity under the viaduct could reduce the accumulation of air pollutants caused by the shelter effect. Additionally, in-street barriers would be beneficial in reducing population exposure to traffic-related emissions by altering the airflows near roads.