Kun Qin

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R², demonstrated high accuracy with values of 0.92 for PM_2.5, 0.95 for PM₁₀, 0.95 for O₃, 0.90 for NO₂, 0.79 for SO₂, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM_2.5, although PM₁₀ exhibited a rebound in northern regions. The concentrations of SO₂ and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O₃ concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution.

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors.

Aim: To date, PM2.5-associated vascular damage in metabolic abnormalities has remained controversial. We knew little about the vascular damage of PM2.5 constituents. Thus, this study aimed to investigate the relationship between long-term exposure to PM2.5 and its constituents and vascular damage in metabolic abnormalities.Methods: A total of 124,387 participants with metabolic abnormalities (defined as at least one metabolic disorder, such as obesity, elevated blood pressure, elevated triglyceride level, elevated fasting glucose level, or low HDL cholesterol level) were recruited in this study from 11 representative centers in China between January 2011 and December 2017. PM2.5 and its constituents (black carbon [BC], organic matter [OM], sulfate [SO42－], nitrate [NO3－], and ammonium salts [NH4＋]) were extracted. Elevated brachial-ankle pulse wave velocity (baPWV) (≥ 1,400 cm/s) and declined ankle-brachial index (ABI) (＜0.9) indicated vascular damage. Multivariable logistic regression and Quantile g-Computation models were utilized to explore the impact on outcomes.Results: Of the 124,387 participants (median age, 49 years), 87,870 (70.64%) were men. One-year lag exposure to PM2.5 and its constituents was significantly associated with vascular damage in single pollutant models. The adjusted odds ratios (OR) for each 1-μg/m3 increase in PM2.5 was 1.013 (95% CI, 1.012–1.015) and 1.031 (95% CI, 1.025–1.037) for elevated baPWV and decreased ABI, respectively. PM2.5 constituents were also associated with vascular damage in multi-pollutant models. Among the PM2.5 constituents, BC (47.17%), SO42－ (33.59%), and NH4＋ (19.23%) have the highest contribution to elevated baPWV and NO3－ (47.89%) and BC (23.50%) to declined ABI.Conclusion: Chronic exposure to PM2.5 and PM2.5 constituents was related to vascular damage in the abnormal metabolic population in China. The heterogeneous contribution of different PM2.5 constituents to vessel bed damage is worthy of attention when developing targeted strategies.

Editorial: Occupational exposure and cardiometabolic disorders