Association between air particulate matter pollution and blood cell counts of women preparing for pregnancy: Baseline analysis of a national birth cohort in China

The Association Between Air Pollution Exposure and White Blood Cell Counts: A Nationwide Cross-Sectional Survey in South Korea

Background: The effect of air pollution, a major global health issue, on the immune system, particularly on white blood cell (WBC) counts, remains underexplored. Methods: This study utilized data from 54,756 participants in the Korean National Health and Nutrition Examination Survey to investigate the effects of short- (day of examination and 7-day averages), mid- (30- and 90-day averages), and long-term (one-, three-, and five-year averages) air pollutant exposure on WBC counts. We assessed exposure to particulate matter (PM10, PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Results: Linear regression with log-transformed WBC counts, adjusted for confounders, showed that PM10 was positively associated with long-term exposure, PM2.5 was negatively associated with short- and mid-term exposures, SO2 was consistently negatively associated with short- and mid-term exposures, NO2 and CO were positive across most periods, and O3 was negatively associated with short- and mid-term exposures. Logistic regression analysis confirmed these findings, showing that short- and mid-term exposure to PM10, PM2.5, and SO2 was negatively associated with the risk of belonging to the high-WBC group, while long-term exposure to PM10, PM2.5, NO2, and CO showed positive associations with risk. Conclusions: Our findings highlight the time- and pollutant-specific associations between air pollution exposure and WBC counts, underscoring air pollution's potential impact on systemic inflammation.

Ambient particulate air pollution, blood cell parameters, and effect modification by psychosocial stress: Findings from two studies in three major Chinese cities

The associations between particulate matter (PM) exposure, psychosocial stress and blood cell parameters are bringing novel insights to characterize the early damage of multiple diseases. Based on two studies conducted in three Chinses cities using cross-sectional (Beijing, 425 participants) and panel study (Tianjin and Shanghai, 92 participants with 361 repeated measurements) designs, this study explored the associations between short-term exposure to ambient PM and blood cell parameters, and the effect modification by psychosocial stress. Increasing PM_2.5 exposure was significantly associated with decreases in red blood cell (RBC) count and mean corpuscular hemoglobin concentration (MCHC), and increases in mean corpuscular volume (MCV), platelets count (PLT) and platelet hematocrit (PCT) in both studies. For instance, a 10 μg/m³ increment in PM_2.5 concentration was associated with a 1.04% (95%CI: 0.16%, 1.92%) increase in PLT (4-d) and a 1.09% (95%CI: 0.31%, 1.87%) increase in PCT (4-d) in the cross-sectional study, and a 0.64% (95%CI: 0.06%, 1.22%) increase in PLT (1-d) and a 0.72% (95%CI: 0.33%, 1.11%) increase in PCT (1-d) in the panel study, respectively. In addition, stronger increases in MCV, PLT, and PCT associated with PM_2.5 exposure were found in higher psychosocial stress group compared to lower psychosocial stress group (p for interaction <0.10), indicating that blood cell parameters of individuals with higher psychosocial stress might be more susceptible to the early damages of PM_2.5 exposure.

Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review

Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.