Tracing the plasma kallikrein-kinin system-activating component in the atmospheric particulate matter with different origins.

Atmospheric particulate matter (PM) perturbs hematological homeostasis by targeting the plasma kallikrein-kinin system (KKS), causing a cascade of zymogen activation events. However, the causative components involved in PM-induced hematological effects are largely unknown. Herein, the standard reference materials (SRMs) of atmospheric PM, including emissions from the diesel (2975), urban (1648a), and bituminous coal (2693), were screened for their effects on plasma KKS activation, and the effective constituent contributing to PM-induced KKS activation was further explored by fraction isolation and chemical analysis. The effects of three SRMs on KKS activation followed the order of 2975 > 1648a > 2693, wherein the fractions of 2975 isolated by acetone and water, together with the insoluble particulate residues, exerted significant perturbations in the hematological homeostasis. The soot contents in the SRMs and corresponding isolated fractions matched well with their hematological effects, and the KKS activation could be dependent on the soot surface oxidation degree. This study, for the first time, uncovered the soot content in atmospheric PM with different origins contributed to the distinct effects on plasma KKS activation. The finding would be of utmost importance for the health risk assessment on inhaled airborne fine PM, given its inevitable contact with human circulatory system.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications.

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

Mass Spectrometry Imaging Strategy for In Situ Quantification of Soot in Size-Segregated Air Samples.

Soot, mainly derived from incomplete combustion of fossil fuel and biomass, exists ubiquitously in different environmental matrixes. To study the detrimental effects of soot on climate, air quality, and human health, accurate quantification of soot is an important prerequisite. However, until now, quantification of soot in environmental media, especially in carbonaceous media, is still very challenging. Here, we report a matrix-free laser desorption/ionization mass spectrometry (LDI-MS) method for in situ imaging of soot particles in size-segregated aerosol samples collected on filter membranes. A series of round-shaped sample spots in filter membranes were selected and subjected to MS imaging analysis, enabling direct in situ quantification of soot without solvent extraction or separation. Especially, the MS imaging with serial sample spots can overcome the problems of sweet-spot in LDI-MS and inhomogeneous distribution of soot in the filter membrane, thus greatly improving the precision of quantification. The limit of detection of soot was 4 ng/m2 and the recovery was 84.4-126%. By using this method, we found that a higher soot content was present in larger-sized particulate matter than smaller-sized particles, suggesting that aerosol soot was mainly derived from primary emission sources. Furthermore, this method also shows the potential to analyze nitrate and sulfate species in PM2.5. To the best of our knowledge, it is the first method capable of simultaneous analysis of inorganic salts and soot in air samples. It represents a novel strategy for in situ quantification of aerosol soot with the advantages of high specificity, high sensitivity, separation-, solvent- and matrix-free.

A pandemic-induced environmental dilemma of disposable masks: solutions from the perspective of the life cycle.

The coronavirus disease 2019 (COVID-19) has swept the world and still afflicts humans. As an effective means of protection, wearing masks has been widely adopted by the general public. The massive use of disposable masks has raised some emerging environmental and bio-safety concerns: improper handling of used masks may transfer the attached pathogens to environmental media; disposable masks mainly consist of polypropylene (PP) fibers which may aggravate the global plastic pollution; and the risks of long-term wearing of masks are elusive. To maximize the utilization and minimize the risks, efforts have been made to improve the performance of masks (e.g., antivirus properties and filtration efficiency), extend their functions (e.g., respiration monitoring and acting as a sampling device), develop new disinfection methods, and recycle masks. Despite that, from the perspective of the life cycle (from production, usage, and discard to disposal), comprehensive solutions are urgently needed to solve the environmental dilemma of disposable masks in both technologies (e.g., efficient use of raw materials, prolonging the service life, and enabling biodegradation) and policies (e.g., stricter industry criteria and garbage sorting).

New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing.

During the SARS period in 2003 and COVID-19 pandemic period in 2020, unexpected severe particulate matter pollution occurred in northern China, although the anthropogenic activities and associated emissions have assumed to be reduced dramatically. This anomalistic increase in PM2.5 pollution raises a question about how source emissions impact the air quality during these pandemic periods. In this study, we investigated the stable Cu and Si isotopic compositions and typical source-specific fingerprints of PM2.5 and its sources. We show that the primary PM2.5 emissions (PM2.5 emitted directly from sources) actually had no reduction but redistribution during these pandemic periods, rather than the previous thought of being greatly reduced. This finding provided critical evidence to interpret the anomalistic PM2.5 increase during the pandemic periods in north China. Our results also suggested that both the energy structure adjustment and stringent regulations on primary emissions should be synergistically implemented in a regional scale for clean air actions in China.

[Recent advances in method development and application of multi-collector inductively coupled plasma mass spectrometry].

Stable isotopic analysis is an important branch of analytical chemistry. Accurate determination of the stable isotopic compositions of substances is critical for tracing their sources and investigating their transformation processes. The new generation of mass spectrometry technology has greatly facilitated the development of high-precision stable isotopic analysis methods. In particular, multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) has emerged as a powerful tool for isotopic composition determination. However, isotopic analysis by MC-ICP-MS is highly sensitive to the sample matrix, which may compromise the precision and accuracy of the analysis. Therefore, it is particularly important to reduce the sample matrix effect using efficient sample purification techniques. This article summarizes the recent progress in sample purification and instrument hyphenation methods for MC-ICP-MS, and provides perspectives on the future application of MC-ICP-MS in different fields.

COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China.

The contradiction between the regional imbalance and an one-size-fits-all policy is one of the biggest challenges in current air pollution control in China. With the recent implementation of first-level public health emergency response (FLPHER) in response to the COVID-19 pandemic in China (a total of 77 041 confirmed cases by February 22, 2020), human activities were extremely decreased nationwide and almost all economic activities were suspended. Here, we show that this scenario represents an unprecedented "base period" to probe the short-term emission control effect of air pollution at a city level. We quantify the FLPHER-induced changes of NO2, SO2, PM2.5, and PM10 levels in 174 cities in China. A machine learning prediction model for air pollution is established by coupling a generalized additive model, random effects meta-analysis, and weather research and forecasting model with chemistry analysis. The short-term control effect under the current energy structure in each city is estimated by comparing the predicted and observed results during the FLPHER period. We found that the short-term emission control effect ranges within 53.0%-98.3% for all cities, and southern cities show a significantly stronger effect than northern cities (P < 0.01). Compared with megacities, small-medium cities show a similar control effect on NO2 and SO2 but a larger effect on PM2.5 and PM10.