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Increasing studies have reported temperature modification effects on air pollutants-induced respiratory diseases. In the current study, daily data of respiratory emergency room visits (ERVs), meteorological factors, and concentrations of air pollutants were collected from 2013 to 2016 in Lanzhou, a northwest city in China. Daily average temperature was stratified into low (≤ 25 percentile, P 25 ), medium (25-75 percentile, P 25 -P 75 ) and high (≥ 75 percentile, P 75 ) to explore how temperature modifies the effects of air pollutants (PM 2.5 , PM 10 , SO 2 , and NO 2 ) on respiratory ERVs by using generalized additive Poisson regression model (GAM). Seasonal modification was also investigated. Results showed that (a) PM 10 , PM 2.5 , and NO 2 had the strongest effects on respiratory ERVs in low temperature; (b) males and 15-and-younger were more vulnerable in low temperature while females and those older than 46 years were highly affected in high temperature; (c) PM 10 , PM 2.5 , and NO 2 were mostly associated with the total and both males and females in winter, while SO 2 resulted in the highest risk for the total and males in autumn and females in spring. In conclusion, this study found significant temperature modification effects and seasonal differences on the risks of respiratory ERVs due to air pollutants in Lanzhou, China., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Ambient air pollution has been regarded as an important cause of the morbidity and mortality of respiratory diseases. In the current work, a total of 469,490 respiratory emergency room (ER) visits in Lanzhou, China from Jan 1, 2013 to Dec 31, 2016 were collected. A generalized additive model (GAM) was used to investigate the association between O 3 and respiratory ER visits for the different gender and age subgroups. The results showed that: (a) with per inter-quartile range (IQR) (31 µg/m 3 ) increase in O 3 , the greatest relative risk (RR) of respiratory ER visits for the total was 1.014 (95% CI 1.008-1.020) at lag 4 days. Females and 16-to-45-year-olds were relatively more sensitive to O 3 ; (b) the significant lag effects were found in single-day lag models, with the highest RR values for different groups were observed at lag 3-lag 5 days. The multi-day cumulative lag effects were stronger for the total; (c) in the multiple-pollutant models, the effects of O 3 were generally increased when introducing other pollutants (PM 10 , PM 2.5 , SO 2 and NO 2 ) for adjustment. This study demonstrated that short-term exposure to O 3 increased the RR of respiratory ER visits in Lanzhou, China., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

In recent years, O 3 pollution had been worsening in China and became a major challenge for human health. To evaluate the O 3 effects on circulatory and respiratory mortality in Harbin, a high latitude city of northeast China, we applied a time-series study from 2014 to 2016. After collecting data and adjusting for the effects of confounders, we built the generalized additive model to assess the associations between O 3 and mortality at different lag days. The results showed that an interquartile-range (IQR) increase in O 3 concentration corresponded to excess risk (ER) of 2.00% (95%CI: - 0.25-4.30%) for circulatory mortality at lag 0 and 8.02% (95%CI: 4.18-12.01%) for respiratory mortality at lag 2 days in the single-pollutant model. Stratified analysis showed that O 3 had a greater effect on females than on males. The effect of O 3 exposure on circulatory mortality was stronger during the warm period, while the opposite trend was founded for respiratory mortality. The sensitivity analysis showed that the effects of O 3 were relatively independent and the major results were robust., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

As one of the most important industrial cities in Northwest China, Lanzhou currently suffers from serious air pollution. This study analyzed the formation mechanism and potential source areas of persistent air pollution in Lanzhou during the heating period from November 1, 2016 to March 31, 2017 based on the air pollutant concentrations and relevant meteorological data. Our findings indicate that particulate pollution was extremely severe during the study period. The daily PM 2.5 and PM 10 concentrations had significantly negative correlations with daily temperature, wind speed, maximum daily boundary layer height, while the daily PM 2.5 and PM 10 concentrations showed significantly positive correlations with daily relative humidity. Five persistent pollution episodes were identified and classified as either stagnant accumulation or explosive growth types according to the mechanism of pollution formation and evolution. The PM 2.5 and PM 10 concentrations and PM 2.5 /PM 10 ratio followed a growing "saw-tooth cycle" pattern during the stagnant accumulation type event. Dust storms caused abrupt peaks in PM 10 and a sharp decrease in the PM 2.5 /PM 10 ratio in explosive growth type events. The potential sources of PM 10 were mainly distributed in the Kumtag Desert in Xinjiang Uygur Autonomous Region, the Qaidam Basin and Hehuang Valley in Qinghai Province, and the western and eastern Hexi Corridor in Gansu Province. The contributions to PM 10 were more than 120 μg/m 3 . The important potential sources of PM 2.5 were located in Hehuang Valley in Qinghai and Linxia Hui Autonomous Prefecture in Gansu; the concentrations of PM 2.5 were more than 60 μg/m 3 ., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Epidemiological studies have suggested that particulate matter (PM) pollution seriously affects human health, particularly it is closely associated with respiratory diseases. The aim of this study is to quantitatively evaluate the effect of PMs (PM 10 and PM 2.5 ) on emergency room (ER) visits for respiratory diseases in Lanzhou, a valley basin city in northwest China. Based on the data of the ER visits, daily concentration of particulate matters and daily meteorological elements from January 1, 2013, to July 31, 2017, we used a generalized additive model (GAM) of time series to evaluate the exposure-response relationship between PMs and respiratory ER visits. Seasonal modified effects of PM 2.5 and PM 10 on different age and gender groups were also performed. Results showed that the highest incidence of respiratory diseases occurred in winter. Respiratory ER visits for the total were significantly associated with PM 2.5 (at lag 0 day) and PM 10 (at lag 3 days), with relative risks (RRs) of 1.042 (95%CI: 1.036 -1.047) and 1.013 (95%CI: 1.011-1.016), respectively. Effects of PM pollutants on respiratory diseases are different among different age and gender groups. Children under 15 years and the elders over 60 years were the most sensitive to PM pollution, and males were more sensitive than females. The results obtained in the current study would provide a scientific evidence for local government to make policy decision for prevention of respiratory diseases., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.)

The outbreak of coronavirus disease 2019 (COVID-19) continues to spread worldwide and has led to recession, rising unemployment, and the collapse of the health-care system. The aim of this study was to explore the exposure-response relationship between daily confirmed COVID-19 cases and environmental factors. We used a time-series generalized additive model (GAM) to investigate the short-term association between COVID-19 and environmental factors by using daily meteorological elements, air pollutant concentration, and daily confirmed COVID-19 cases from January 21, 2020, to February 29, 2020, in Shanghai, China. We observed significant negative associations between daily confirmed COVID-19 cases and mean temperature (T ave ), temperature humidity index (THI), and index of wind effect (K), whereas air quality index (AQI), PM 2.5 , PM 10 NO 2 , and SO 2 were significantly associated with the increase in daily confirmed COVID-19 cases. A 1 °C increase in T ave , one-unit increase in THI, and 10-unit increase in K (lag 0-7 days) were associated with 4.7, 1.8, and 1.6% decrease in daily confirmed cases, respectively. Daily T ave , THI, K, PM 10 , and SO 2 had significant lag and persistence (lag 0-7 days), whereas the lag and persistence of AQI, PM 2.5 , and NO 2 were significant at both lag 0-7 and 0-14 days. A 10-μg/m 3 increase in PM 10 and 1-μg/m 3 increase in SO 2 was associated with 13.9 and 5.7% increase in daily confirmed cases at lag 0-7 days, respectively, whereas a 10-unit increase in AQI and a 10-μg/m 3 increase in PM 2.5 and NO 2 were associated with 7.9, 7.8, and 10.1% increase in daily confirmed cases at lag 0-14 days, respectively. Our findings have important implications for public health in the city of Shanghai., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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]