Air pollution prevention in central China: Effects on particulate-bound PAHs from 2010 to 2018

Associations of polycyclic aromatic hydrocarbons exposure with perinatal anxiety symptoms

BACKGROUND

Polycyclic aromatic hydrocarbons (PAHs) have recently garnered attention for their possible neurotoxic effects. This study was meticulously crafted to assess the influence of PAHs exposure on the emergence of perinatal anxiety symptoms.

METHODS

From April 28, 2020, to July 20, 2021, a case-control study recruiting eligible pregnant women was conducted in two primary hospitals in Hefei City, China. Professionals employed the 7-item Generalized Anxiety Disorder Scale to assess the participants' anxiety symptoms during pregnancy and postpartum via WeChat. Urinary concentrations of 12 hydroxylated PAH metabolites during pregnancy and postpartum were quantified through gas chromatography-tandem triple quadrupole mass spectrometry. Logistic regression analysis and mixed exposure modeling (BKMR model) were employed in our study to probe into the associations between PAHs exposure and perinatal anxiety symptoms.

RESULTS

Our study incorporated 642 participants (279 cases and 363 controls). Multivariable logistic regression models revealed significant dose-response relationships between the levels of individual PAH metabolites in urine and prenatal anxiety symptoms. Compared to pregnant women in the lowest exposure tertile, those in the highest tertiles of urinary concentrations of 2-OHNA, 9-OHFLU, ∑OHFLU, 2-OHDBF, and ∑OH-PAHs had increased risk of experiencing prenatal anxiety (OR = 1.915, 95%CI: 1.271-2.886; OR = 2.084, 95%CI: 1.358-3.199; OR = 2.055, 95%CI: 1.355-3.117; OR = 1.675, 95%CI: 1.119-2.507; OR = 1.870, 95%CI: 1.228-2.847, respectively). BKMR analysis indicated a significant trend of increasing likelihood of prenatal anxiety symptoms with higher levels of the OH-PAHs mixture. Meanwhile, follow-up of 230 pregnant women until 42 days postpartum revealed that increased prenatal urinary concentrations of 2-OHFLU and ∑OHFLU were associated with a higher risk of postpartum anxiety symptoms (OR = 2.101, 95%CI: 1.000-4.414 for the medium vs. low 2-OHFLU exposure; OR = 2.277, 95%CI: 1.080-4.799 for the high vs. low ∑OHFLU exposure, respectively).

CONCLUSIONS

Our study brings to light a potentially strong positive link between PAHs exposure and perinatal anxiety symptoms.

Source-specific probabilistic health risk assessment of dust PAHs in urban parks based on positive matrix factorization and Monte Carlo simulation

Understanding the health risks of polycyclic aromatic hydrocarbons (PAHs) in dust from city parks and prioritizing sources for control are essential for public health and pollution management. The combination of Source-specific and Monte Carlo not only reduces management costs, but also improves the accuracy of assessments. To evaluate the sources of PAHs in urban park dust and the possible health risks caused by different sources, dust samples from 13 popular parks in Kaifeng City were analyzed for PAHs using gas chromatograph-mass spectrometer (GC-MS). The results showed that the surface dust PAH content in the study area ranged from 332.34 µg·kg-1 to 7823.03 µg·kg-1, with a mean value of 1756.59 µg·kg-1. Nemerow Composite Pollution Index in the study area ranged from 0.32 to 14.41, with a mean of 2.24, indicating that the overall pollution warrants attention. Four pollution sources were identified using the positive matrix factorization (PMF) model: transportation source, transportation-coal and biomass combustion source, coke oven emission source, and petroleum source, with contributions of 33.74%, 25.59%, 22.14%, and 18.54%, respectively. The Monte Carlo cancer risk simulation results indicated that park dust PAHs pose a potential cancer risk to all three populations (children, adult male and adult female). Additionally, the cancer risk for children was generally higher than that for adult males and females, with transportation sources being the main contributor to the carcinogenic risk. Lastly, sensitivity analyses results showed that the toxic equivalent concentration (CS) is the parameter contributing the most to carcinogenic risk, followed by Exposure duration (ED).

Spatiotemporal characteristics analysis of multi-factorial air pollution in the Jing-Jin-Ji region based on improved sequential ICI method and novel grey spatiotemporal incidence models

Air pollution shares the attributes of multi-factorial influence and spatiotemporal complexity, leading to air pollution control assistance models easily falling into a state of failure. To address this issue, we design a framework containing improved data fusion method, novel grey incidence models and air pollution spatiotemporal analysis to analyze the complex characteristics of air pollution under the fusion of multiple factors. Firstly, we improve the existing data fusion method for multi-factor fusion. Subsequently, we construct two grey spatiotemporal incidence models to examine the spatiotemporal characteristics of multi-factorial air pollution in network relationships and changing trends. Furthermore, we propose two new properties that can manifest the performance of grey incidence analysis, and we provide detailed proof of the properties of the new models. Finally, in the Jing-Jin-Ji region, the novel models are used to study the network relationships and trend changes of air pollution. The findings are as follows: (1) Two highly polluted belts in the region require attention. (2) Although the air pollution network under multi-factorial fusion obeys the first law of geography, the network density and node density exhibit significant variations. (3) From 2013 to 2021, all pollutants except O3 show improvement. (4) Recommendations for responses are presented based on the above-mentioned results. (5) The parameter analyses, model comparisons, Monte Carlo experiments and model feature summaries illustrate that the proposed models are practical, interpretable and considerably outperform various prevailing competitors with remarkable universality.