Objective To investigate the association of ambient air pollutants with blood physiological and biochemical indicators and systemic immune inflammation index in people living around the Beijing metropolitan area and to provide a basis for formulating intervention policies on the health hazards of air pollution.

Methods Data on demographics and blood physiological and biochemical indicators of 1 150 residents (8 – 90 years old) living around the Beijing metropolitan area, as well as monitoring data of air pollutants in the area for the period of 2018 – 2019, were collected from the Air Pollution and Population Health Database of the National Population Health Science Data Center. Correlation analysis was performed and multiple linear regression and restricted cubic spline (RCS) models were applied to assess the association of air pollutants with blood physiological and biochemical indicators and systemic immune inflammation index (SIRI).

Results Spearman correlation analysis showed that the concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM_2.5) and carbon monoxide (CO) were positively correlated with monocyte count (MO#), eosinophil count (EOS#), and red blood cell distribution width standard deviation (RDW-SD), and negatively correlated with mean corpuscular hemoglobin concentration (MCHC) and platelet crit (PCT) (all P < 0.05). Particulate matter with an aerodynamic diameter of less than 10 μm (PM₁₀) concentration was positively correlated with MO# and SIRI, and negatively correlated with mean corpuscular hemoglobin (MCH) and MCHC (all P < 0.01). Nitrogen dioxide (NO₂) concentration was positively correlated with MO# and RDW-SD, and negatively correlated with MCHC and PCT (all P < 0.01). Sulfur dioxide (SO₂) concentration was positively correlated with RDW-SD, and negatively correlated with neutrophil count (NEUT#) and MCHC (all P < 0.05). Ozone (O₃) concentration was negatively correlated with EOS#, MCH, and MCHC (all P < 0.01). After adjusting for confounders such as gender and age, multiple linear regression analysis showed that for every 1 μg/m³ increase in PM_2.5 concentration, MCHC decreased by 2.947 g/L; for every 1 μg/m³ increase in PM₁₀ concentration, NEUT#, MO#, and SIRI increased by 0.236 × 109/L, 0.025 × 109/L, and 0.112, respectively; for each 1 μg/m³ increase in NO₂ concentration, MO#, RDW-SD, and SIRI increased by 0.011×109/L, 0.136 fL, and 0.023, respectively, and MCHC decreased by 2.743 g/L; for every 1 μg/m³ increase in SO₂ concentration, MCHC decreased by 1.871 g/L; for every 1 μg/m³ increase in CO concentration, MCHC and RDW-SD decreased by 1.383 g/L and 0.460 fL, respectively; for each 1 μg/m³ increase in O₃ concentration, MO#, RDW-SD, and SIRI increased by 0.005×109/L, 0.113 fL, and 0.005, respectively, and MCHC decreased by 1.245 g/L. RCS model analysis showed an inverted U-shaped relationship between PM₁₀ concentration and MO# and MCH, and between O₃ concentration and SIRI (all P for nonlinearity < 0.05). A U-shaped relationship was observed between PM_2.5 concentration and MO#, between PM₁₀ concentration and PCT, and between CO concentration and MO# (all P for nonlinearity < 0.05).

Conclusions Long-term exposure to air pollutants such as PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ may increase the risk of systemic immune inflammation in the exposed population.