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Particulate matter (PM) has been proved to be a risk factor for the development of circulatory system diseases (CSDs) around the world. In this study, we collected daily air pollutants, emergency room (ER) visits for CSDs, and meteorological data from 2009 to 2012 in Beijing, China. After controlling for the long-term trend and eliminating the influence of confounding factors, the generalized additive model (GAM) was used to evaluate the short-term effects of PM 10 on CSDs and cause-specific diseases. The results showed that for every 10 μg/m 3 increase in PM 10 , the largest effect estimates in ER visits of total CSDs, arrhythmia, cerebrovascular diseases, high blood pressure, ischemic heart disease and other related diseases were 0.14% (95% CI: 0.06-0.23%), 0.37% (95% CI: - 0.23 to 0.97%), 0.20% (95% CI: 0.00-0.40%), 0.15% (95% CI: 0.02-0.27%), 0.18% (95% CI: 0.02-0.35%) and 0.35% (95% CI: - 0.04 to 0.79%), respectively. When NO 2 or SO 2 was added into the model, the effect estimates of PM 10 were mostly attenuated, while in those models with PM 2.5 added, the effect estimates of PM 10 were mostly increased. Stratified analysis indicated that PM 10 had a greater effect on males and the elderly.

Particulate matter (PM) has been proved to be a risk factor for the development of circulatory system diseases (CSDs) around the world. In this study, we collected daily air pollutants, emergency room (ER) visits for CSDs, and meteorological data from 2009 to 2012 in Beijing, China. After controlling for the long-term trend and eliminating the influence of confounding factors, the generalized additive model (GAM) was used to evaluate the short-term effects of PM10 on CSDs and cause-specific diseases. The results showed that for every 10 μg/m3 increase in PM10, the largest effect estimates in ER visits of total CSDs, arrhythmia, cerebrovascular diseases, high blood pressure, ischemic heart disease and other related diseases were 0.14% (95% CI: 0.06–0.23%), 0.37% (95% CI: − 0.23 to 0.97%), 0.20% (95% CI: 0.00–0.40%), 0.15% (95% CI: 0.02–0.27%), 0.18% (95% CI: 0.02–0.35%) and 0.35% (95% CI: − 0.04 to 0.79%), respectively. When NO2 or SO2 was added into the model, the effect estimates of PM10 were mostly attenuated, while in those models with PM2.5 added, the effect estimates of PM10 were mostly increased. Stratified analysis indicated that PM10 had a greater effect on males and the elderly. [ABSTRACT FROM AUTHOR]

Particulate matter with a diameter of ≤2.5 μm (PM 2.5) is a critical air pollutant that adversely affects human health and the ecological environment. Using the data of daily air pollutant concentration and meteorological elements from 2013 to 2020 in Lanzhou, China, we employed the generalized additive model (GAM) and gradient boosting machine (GBM) approaches to analyze the relationship between PM 2.5 concentration and environmental factors. The results revealed that the annual average PM 2.5 concentration in Lanzhou, China, decreased by 3.98 μg/m3 per year during the study period and the variation of PM 2.5 concentration was influenced by many factors. Mean temperature, air pressure, relative humidity, and O 3 concentration had significantly negative effects on PM 2.5 concentration, whereas CO and NO 2 concentrations had significant positive effects. PM 2.5 concentration was also high during static winds or high-speed winds. Among these influencing factors, the interactions of meteorological factors and air pollution also had strong correlations with PM 2.5 concentration. We found that the multi-factor model could better explain the influence of environmental factors on PM 2.5 concentration than the single-factor model. The fitting degree of the GBM was better than that of the GAM model; their mean absolute errors were 11.30 and 11.85, respectively. We also found that the GBM model was more precise for estimating daily PM 2.5 concentration, whereas the GAM was more suitable for long-term trend analysis. • The variation of PM 2.5 concentration is influenced by multiple factors and their interactions. • The multi-factor model could better explain the influence of environmental factors on PM 2.5 concentration. • The fitting degree of the GBM was better than that of the GAM model. • GBM was more suitable for short-term prediction, whereas GAM was more suitable for long-term trend analysis. [ABSTRACT FROM AUTHOR]