Health risk assessment and source apportionment of PM2.5-bound toxic elements in the industrial city of Siheung, Korea

PM10-bound microplastics and trace metals: A public health insight from the Korean subway and indoor environments

Inhalable airborne microplastics (MPs) presented in indoor and outdoor environments, can deeply penetrate the lungs, potentially triggering inflammation and respiratory illnesses. The present study aims to evaluate human health risks from respirable particulate matter (PM)-bound trace metals and MPs in indoor (SW- subway and IRH- indoor residential houses) and outdoor (OD) environments. This research provides an initial approach to human respiratory tract (HRT) mass depositions of PM10-bound total MPs and nine specific MP types to predict potential human health threats from inhalation exposure. Results indicate that PM-bound trace metals and MPs were around 4 times higher in SW microenvironments compared to OD locations. In IRH, cancer risk (CR) levels were estimated 9 and 4 times higher for PM10 and PM2.5, respectively. Additionally, MP particle depositions per gram of lung cell weight were highest in IRH (23.77), followed by OD and SW. Whereas, lifetime alveoli depositions of MPs were estimated at 13.73 MP/g, which exceeds previously reported respiratory disease fatality cases by 10 to 5 times. Prolonged exposure duration at IRH emerged as a key factor contributing to increased CR and MP lung deposition levels. This research highlights severe lung risks from inhaling PM-bound MPs and metals, offering valuable health insights.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

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.

Elemental composition of atmospheric PM10 during COVID-19 lockdown and recovery periods in Moscow (April-July 2020)

Changes in the concentrations of PM10-bound potentially toxic elements (PTEs) during the COVID-19 lockdown period and after the revocation of restrictions were analyzed using the data received at the Aerosol Complex of Moscow State University in April-July 2020. During the lockdown, the input of biomass combustion products enriched in PTEs from the Moscow region hindered the decrease in pollutant concentrations. After the introduction of the self-isolation regime, lower concentrations of most PTEs occurred due to the decrease in anthropogenic activity and the rainy meteorological conditions. After the revocation of restrictive measures, the PTE concentrations began to increase. Multivariate statistical analysis (APCA-MLR) identified the main sources of atmospheric pollutants as urban dust, non-exhaust traffic emissions, and combustion and exhaust traffic emissions. PM10 particles were significantly enriched with Sb, Cd, Sn, Bi, S, Pb, Cu, Mo, and Zn. The total non-carcinogenic and carcinogenic risks, calculated according to the U.S. EPA model, decreased by 24% and 23% during the lockdown; after the removal of restrictions, they increased by 61% and 72%, respectively. The study provides insight into the PTE concentrations and their main sources at different levels of anthropogenic impact.

Pollution characteristics and human health risks of PM2.5-bound heavy metals: a 3-year observation in Suzhou, China

This study aimed to analyze the temporal trends, pollution levels, and health risks associated with eleven PM_2.5-bound heavy metals (Sb, Al, As, Hg, Cd, Cr, Mn, Ni, Pb, Se and Tl). A total of 504 PM_2.5 samples were collected in Suzhou from January 2019 to December 2021. The pollution levels were estimated based on enrichment factors (EFs) which can be used to calculate the enrichment of heavy metals in PM_2.5 and determine whether the concentrations of PM_2.5-bound heavy metals are influenced by the crustal or anthropogenic sources, and the health risk of PM_2.5-bound heavy metals via inhalation was assessed following US EPA's Risk Assessment Guidance for Superfund (RAGS). The annual average concentration of PM_2.5 was 46.76 μg m^-3, which was higher than the WHO recommended limit of 5 μg m^-3. The average of the sum of eleven PM_2.5-bound heavy metals was 180.61 ng m^-3, dominated by Al, Mn, and Pb. The concentration of PM_2.5 in 2020 was significantly lower than that in 2019 and 2021. The PM_2.5 and PM_2.5-bound heavy metal concentrations in winter and spring were significantly higher than those in autumn and summer. The EF of As, Cr, Cd, Hg, Ni, Pb, Sb, Mn, Se, and Tl was higher than 10, indicating they were mainly from anthropogenic sources. Exposure to a single non-carcinogenic heavy metal via inhalation was unlikely to cause non-carcinogenic effects (HQ < 1), but the integrated non-carcinogenic risks should be taken seriously (HI > 1). The cumulative carcinogenic risks from the carcinogenic elements were exceeding the lower limit (1 × 10^-6) of the acceptable risk range. The carcinogenic risks of As and Cr(VI) contributed 60.98% and 26.77%, respectively, which were regarded as two key carcinogenic risk factors. Overall, the government policies and countermeasures for the PM_2.5 pollution control should be performed not only based on the PM_2.5 concentration but also based on the PM_2.5-bound heavy metals and their health risks for the local residents.

Source apportionment of PM2.5 using organic/inorganic markers and emission inventory evaluation in the East Mediterranean-Middle East city of Beirut

Source contributions to PM_2.5 concentrations were evaluated in Greater Beirut (Lebanon), a typical East Mediterranean-Middle East (EMME) city, using Positive Matrix Factorization with two approaches. The first approach included only inorganic species (PMF-trad) and the other approach added organic markers (PMF-org). PMF-org identified 4 additional sources, and large discrepancies in contributions were observed for some major sources found in both approaches, highlighting the importance of including organic markers. The traffic factor was underestimated in PMF-trad by 2 to 7 folds. Moreover, results showed that this city is prone to high desert dust concentrations originating from uncontrollable dust storm events, like all cities in the Middle East. A PM_2.5 mitigation plan taking into account the potency of the identified sources was developed. Sources like diesel generators or traffic presented smaller contributions in term of mass compared to desert dust, however the health impact of the latter is relatively small and actions should target sources with the highest potency. Local emission inventories in the EMME region are scarce and studies typically rely on global emission inventories for local air quality management plans, but these inventories significantly underestimate Beirut's road transport emissions by more than an order of magnitude.