This study reviewed the prediction of fine particulate matter (PM_2.5) from satellite aerosol optical depth (AOD) and summarized the advantages and limitations of these predicting models. A total of 116 articles were included from 1436 records retrieved. The number of such studies has been increasing since 2003. Among these studies, four predicting models were widely used: Multiple Linear Regression (MLR) (25 articles), Mixed-Effect Model (MEM) (23 articles), Chemical Transport Model (CTM) (16 articles) and Geographically Weighted Regression (GWR) (10 articles). We found that there is no so-called best model among them and each has both advantages and limitations. Regarding the prediction accuracy, MEM performs the best, while MLR performs worst. CTM predicts PM_2.5 better on a global scale, while GWR tends to perform well on a regional level. Moreover, prediction performance can be significantly improved by combining meteorological variables with land use factors of each region, instead of only considering meteorological variables. In addition, MEM has advantages in dealing with the AOD data with missing values. We recommend that with the help of higher resolution AOD data, future works could be focused on developing satellite-based predicting models for the prediction of historical PM_2.5 and other air pollutants.

Background: Previous studies have suggested that exposure to air pollution may increase stroke risk, but the results remain inconsistent. Evidence of more recent studies is highly warranted, especially gas air pollutants. Methods: We searched PubMed, Embase, and Web of Science to identify studies till February 2020 and conducted a meta-analysis on the association between air pollution (PM_2.5, particulate matter with aerodynamic diameter less than 2.5 μm; PM₁₀, particulate matter with aerodynamic diameter less than 10 μm; NO₂, nitrogen dioxide; SO₂, sulfur dioxide; CO, carbon monoxide; O₃, ozone) and stroke (hospital admission, incidence, and mortality). Fixed- or random-effects model was used to calculate pooled odds ratios (OR)/hazard ratio (HR) and their 95% confidence intervals (CI) for a 10 μg/m³ increase in air pollutant concentration. Results: A total of 68 studies conducted from more than 23 million participants were included in our meta-analysis. Meta-analyses showed significant associations of all six air pollutants and stroke hospital admission (e.g., PM_2.5: OR = 1.008 (95% CI 1.005, 1.011); NO₂: OR = 1.023 (95% CI 1.015, 1.030), per 10 μg/m³ increases in air pollutant concentration). Exposure to PM_2.5, SO₂, and NO₂ was associated with increased risks of stroke incidence (PM_2.5: HR = 1.048 (95% CI 1.020, 1.076); SO₂: HR = 1.002 (95% CI 1.000, 1.003); NO₂: HR = 1.002 (95% CI 1.000, 1.003), respectively). However, no significant differences were found in associations of PM₁₀, CO, O₃, and stroke incidence. Except for CO and O₃, we found that higher level of air pollution (PM_2.5, PM₁₀, SO₂, and NO₂) exposure was associated with higher stroke mortality (e.g., PM₁₀: OR = 1.006 (95% CI 1.003, 1.010), SO₂: OR = 1.006 (95% CI 1.005, 1.008). Conclusions: Exposure to air pollution was positively associated with an increased risk of stroke hospital admission (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃), incidence (PM_2.5, SO₂, and NO₂), and mortality (PM_2.5, PM₁₀, SO₂, and NO₂). Our study would provide a more comprehensive evidence of air pollution and stroke, especially SO₂ and NO₂.

Increasing studies have discussed how ambient air pollution affects cognitive function, however, the results are inconsistent, and such studies are limited in developing countries. To fill the gap, in this study, we aimed to explore the effect of ambient particulate matters (PM₁, PM_2.5, PM₁₀) on cognitive function of middle-aged and elderly Chinese adults. A total of 7928 participants older than 45 were included from CHARLS collected in 2011, 2013, and 2015. Cognitive function was evaluated with two dimensions, the first one was episodic memory and the second dimension was mental status. The total score of cognitive function was the sum of above two dimensions (0–31 points). Participants' exposure to ambient particulate matters was estimated by using a satellite-based spatiotemporal model. Linear mixed models were applied to analyze the impact of PM₁, PM_2.5, and PM₁₀ on cognition function. Further interaction analyses were applied to examine the potential effect modifications on the association. After adjusting for confounding factors, we found an IQR increase in all three ambient particulate matters was significantly associated with a decrease in cognitive function score, with the greatest effect in the 90-day exposure window for PM₁ (β = −0.227, 95%CI: −0.376, −0.078) and PM_2.5 (β = −0.220, 95%CI: −0.341, −0.099). For ambient PM₁₀, the most significant exposure window was 60-day (β = −0.158, 95%CI: −0.274, −0.042). Interaction analyses showed that the PM-cognitive function association could be modified by gender, region, alcohol consumption, smoking, education level, chronic diseases, and depressive symptoms. In conclusion, exposure to ambient particulate matter for a certain period would significantly decrease cognitive function among middle-aged and elderly Chinese. Furthermore, individuals who were female, or lived in the midland of China were more susceptible to the adverse effect of particulate matters.

Studies on the hypertensive effect of long-term air pollution exposure were inconclusive and showed scarce evidence from rural areas in developing countries. In this context, we examined the associations of air pollution exposure with hypertension and blood pressure, and their effect modifiers in rural Chinese adults. We studied 39,259 participants from a cohort established in five rural regions of central China. Individual exposures to PM2.5 and PM10 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm and 10 μm) and nitrogen dioxide (NO2) was evaluated using satellite-based spatiotemporal models. Mixed-effect regression models were applied to examine the associations of long-term exposure to air pollution with hypertension and four blood pressure component measurements, including systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP). Several potential effect modifiers related to demographic and behavioral factors were also examined. The results showed that for each 1 μg/m3 increase in PM2.5, PM10 and NO2, the adjusted odds ratio of hypertension was 1.029 (95%CI: 1.001,1.057), 1.015 (95%CI: 1.001, 1.029) and 1.069 (95%CI: 1.038, 1.100), respectively. These three air pollutants were also associated with increased SBP (except for PM10), DBP and MAP. The hypertensive effects of air pollution were more pronounced among males, smokers, drinkers, individuals with a high-fat diet, and those with high-level physical activity. Long-term exposure to PM2.5, PM10 and NO2 was associated with increased blood pressure and hypertension in rural Chinese adults, and the associations were modified by several behavioral factors.