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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, P25), medium (25–75 percentile, P25–P75) and high (≥ 75 percentile, P75) to explore how temperature modifies the effects of air pollutants (PM2.5, PM10, SO2, and NO2) on respiratory ERVs by using generalized additive Poisson regression model (GAM). Seasonal modification was also investigated. Results showed that (a) PM10, PM2.5, and NO2 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) PM10, PM2.5, and NO2 were mostly associated with the total and both males and females in winter, while SO2 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. [ABSTRACT FROM AUTHOR]

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 O3 and respiratory ER visits for the different gender and age subgroups. The results showed that: (a) with per inter-quartile range (IQR) (31 µg/m3) increase in O3, 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 O3; (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 O3 were generally increased when introducing other pollutants (PM10, PM2.5, SO2 and NO2) for adjustment. This study demonstrated that short-term exposure to O3 increased the RR of respiratory ER visits in Lanzhou, China. [ABSTRACT FROM AUTHOR]

Previous studies have reported that daily average temperature is connected with respiratory diseases (RD), but proof is limited for the influence of the extreme low temperature on RD in Lanzhou, a northwestern China of temperate area. Generalized additive model (GAM) was built in this work to describe the relationship between daily mean temperature and RD in Lanzhou, China from 2012 to 2017. The results indicated that the exposure–response curve was inverse J-shaped, showing the lower the temperature, the larger the relative risk (RR). The RR of daily emergency room (ER) admissions in P5 extreme low temperature (the temperature below the fifth percentile, etc.) was larger than that in P10. The P5 extreme low temperature has the strongest effect at lag 0, and the RRs were 1.043 (95% CI: 1.030, 1.055) for the total, 1.031 (95% CI: 1.015, 1.046) for males and 1.058 (95% CI: 1.039, 1.077) for females. For different age groups, the largest RRs were 1.026 (95% CI: 1.013, 1.039) for the children (age < 16 years) at lag 5, 1.057 (95% CI: 1.030, 1.085) for the young adults (aged 16–45 years), 1.060 (95% CI: 1.023, 1.099) for the middle-aged (aged 46–60 years) and 1.121 (95% CI: 1.077, 1.166) for the elderly group of age > 60 years. Meanwhile, females and the elderly were more vulnerable to extreme temperature. The results could strengthen the scientific evidence of effects of extreme low temperature on RD in temperate areas. [ABSTRACT FROM AUTHOR]

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 PM2.5 and PM10 concentrations had significantly negative correlations with daily temperature, wind speed, maximum daily boundary layer height, while the daily PM2.5 and PM10 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 PM2.5 and PM10 concentrations and PM2.5/PM10 ratio followed a growing "saw-tooth cycle" pattern during the stagnant accumulation type event. Dust storms caused abrupt peaks in PM10 and a sharp decrease in the PM2.5/PM10 ratio in explosive growth type events. The potential sources of PM10 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 PM10 were more than 120 μg/m3. The important potential sources of PM2.5 were located in Hehuang Valley in Qinghai and Linxia Hui Autonomous Prefecture in Gansu; the concentrations of PM2.5 were more than 60 μg/m3. [ABSTRACT FROM AUTHOR]

Under the global climate warming, extreme weather events occur more and more frequently. Epidemiological studies have proved that extreme temperature is strongly correlated with respiratory diseases. We evaluated extreme-temperature effect on respiratory emergency room (ER) visits for 5 years in Lanzhou, a northwest temperate climate city of China from January 1st, 2013, to August 31st, 2017. We built a distributed lag non-linear model (DLNM) to evaluate the lag effect up to 30 days. Results showed the relative risk (RR) of respiratory disease always reached the maximum at lag 0 day and decreased to 1.0 at lag 5 days. Extremely low temperature showed the lag effect of 22 days and the maximum RR was 1.415 (95% CI 1.295–1.546) at lag 0 day. Extremely high temperature showed the lag effect of 7 days and the maximum RR was 1.091 (95% CI 1.069–1.114) at lag 0 day. The elders (age > 65 years) were at the greatest risk to extreme temperatures and the response were very acute. Children (age ≤ 15 years) were at the lowest risk but the lag effect lasted the longest lag days than other subgroups. Males showed longer-term lag effect and higher RR than females. Our study indicated that the extremely low temperature has a significantly greater effect on respiratory diseases than extremely high temperature. [ABSTRACT FROM AUTHOR]

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 (PM10 and PM2.5) 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 PM2.5 and PM10 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 PM2.5 (at lag 0 day) and PM10 (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. [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]