In order to identify the sources of indoor PM_2.5 and to check which factors influence the concentration of indoor PM_2.5 and chemical elements, indoor concentrations of PM_2.5 and its related elements in residential houses in Beijing were explored. Indoor and outdoor PM_2.5 samples that were monitored continuously for one week were collected. Indoor and outdoor concentrations of PM_2.5 and 15 elements (Al, As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Pb, Se, Tl, V, Zn) were calculated and compared. The median indoor concentration of PM_2.5 was 57.64 μg/m³. For elements in indoor PM_2.5, Cd and As may be sensitive to indoor smoking, Zn, Ca and Al may be related to indoor sources other than smoking, Pb, V and Se may mainly come from outdoor. Five factors were extracted for indoor PM_2.5 by factor analysis, explained 76.8% of total variance, outdoor sources contributed more than indoor sources. Multiple linear regression analysis for indoor PM_2.5, Cd and Pb was performed. Indoor PM_2.5 was influenced by factors including outdoor PM_2.5, smoking during sampling, outdoor temperature and time of air conditioner use. Indoor Cd was affected by factors including smoking during sampling, outdoor Cd and building age. Indoor Pb concentration was associated with factors including outdoor Pb and time of window open per day, building age and RH. In conclusion, indoor PM_2.5 mainly comes from outdoor sources, and the contributions of indoor sources also cannot be ignored. Factors associated indoor and outdoor air exchange can influence the concentrations of indoor PM_2.5 and its constituents.

There were conflictions and differences among the results of cross-sectional studies association between PM_2.5 and COPD prevalence. We aimed to explore the real association between outdoor PM_2.5 and COPD prevalence, analyze the possible cause to the differences and conflictions in previous cross-sectional studies. Cross-sectional literatures about the association between outdoor PM_2.5 and COPD prevalence were selected up to 12 September 2018. Subgroup analysis was performed to explore the source of the heterogeneity. Publication bias was tested via funnel plot. Leave-one-out method was used to conduct influential analysis. Variance analysis was used to analyze the influence of concentration, literature quality and age (over 60 or not) on the ln (aOR) values. The initial search revealed 230 studies, of which 8 were selected. The heterogeneity in this study was significant (I²=62, P<0.01), and random effects model was used. The pooled OR for the association between PM_2.5 and COPD prevalence is 2.32(95%CI, 1.91-2.82). There was no evidence of publication bias. Subgroup analysis showed the subgroup of age seemed to be the source of heterogeneity (P=0.0143, residual I²=0%). Variance analysis showed that the differences of ln (aOR) among each concentration group(p=0.0075) were statistically significant, the same as age groups(P=0.0234). This meta-analysis study demonstrated a conclusive association between PM_2.5 and prevalence of COPD (OR: 2.32, 95%CI 1.91–2.82). The significant heterogeneity among selected studies was mainly caused by age (over 60 or not). High PM_2.5 concentration should be needed in further research of the relationship between PM_2.5 and chronic diseases.

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM_2.5, PM₁₀, SO₂, and CO during the lockdown reduced, but the reductions were not very significant. NO₂ levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K⁺ and lowest Ca²⁺ concentrations in PM_2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O₃ level (increased by 160%) and OC/EC ratios in PM_2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

Monte Carlo simulation (MCS) is a computational technique widely used in exposure and risk assessment. However, the result of traditional health risk assessment based on the MCS method has always been questioned due to the uncertainty introduced in parameter estimation and the difficulty in result validation. Herein, data from a large-scale investigation of individual polycyclic aromatic hydrocarbon (PAH) exposure was used to explore the key factors for improving the MCS method. Research participants were selected using a statistical sampling method in a typical PAH polluted city. Atmospheric PAH concentrations from 25 sampling sites in the area were detected by GC-MS and exposure parameters of participants were collected by field measurement. The incremental lifetime cancer risk (ILCR) of participants was calculated based on the measured data and considered to be the actual carcinogenic risk of the population. Predicted risks were evaluated by traditional assessment method based on MCS and three improved models including concentration-adjusted, age-stratified, and correlated-parameter-adjusted Monte Carlo methods. The goodness of fit of the models was evaluated quantitatively by comparing with the actual risk. The results showed that the average risk derived by traditional and age-stratified Monte Carlo simulation was 2.6 times higher, and the standard deviation was 3.7 times higher than the actual values. In contrast, the predicted risks of concentration- and correlated-parameter-adjusted models were in good agreement with the actual ILCR. The results of the comparison suggested that accurate simulation of exposure concentration and adjustment of correlated parameters could greatly improve the MCS. The research also reveals that the social factors related to exposure and potential relationship between variables are important issues affecting risk assessment, which require full consideration in assessment and further study in future research.