scholarly journals Temporal Cross-Correlations between Ambient Air Pollutants and Seasonality of Tuberculosis: A Time-Series Analysis

2019 ◽  
Vol 16 (9) ◽  
pp. 1585 ◽  
Author(s):  
Hua Wang ◽  
Changwei Tian ◽  
Wenming Wang ◽  
Xiaoming Luo
Keyword(s):  
Time Series ◽  
Air Pollutants ◽  
Quality Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Ambient Air ◽  
Delayed Effect ◽  
Incidence Data ◽  
Ambient Air Pollutants ◽  
Cross Correlations

The associations between ambient air pollutants and tuberculosis seasonality are unclear. We assessed the temporal cross-correlations between ambient air pollutants and tuberculosis seasonality. Monthly tuberculosis incidence data and ambient air pollutants (PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2)) and air quality index (AQI) from 2013 to 2017 in Shanghai were included. A cross-correlogram and generalized additive model were used. A 4-month delayed effect of PM2.5 (0.55), PM10 (0.52), SO2 (0.47), NO2 (0.40), CO (0.39), and AQI (0.45), and a 6-month delayed effect of O3 (−0.38) on the incidence of tuberculosis were found. The number of tuberculosis cases increased by 8%, 4%, 18%, and 14% for a 10 μg/m3 increment in PM2.5, PM10, SO2, and NO2; 4% for a 10 unit increment in AQI; 8% for a 0.1 mg/m3 increment in CO; and decreased by 4% for a 10 μg/m3 increment in O3. PM2.5 concentrations above 50 μg/m3, 70 μg/m3 for PM10, 16 μg/m3 for SO2, 47 μg/m3 for NO2, 0.85 mg/m3 for CO, and 85 for AQI, and O3 concentrations lower than 95 μg/m3 were positively associated with the incidence of tuberculosis. Ambient air pollutants were correlated with tuberculosis seasonality. However, this sort of study cannot prove causality.

scholarly journals Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

2018 ◽  
Vol 15 (10) ◽  
pp. 2160 ◽  
Author(s):  
Lisha Luo ◽  
Yunquan Zhang ◽  
Junfeng Jiang ◽  
Hanghang Luan ◽  
Chuanhua Yu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Respiratory Disease ◽  
Air Pollutants ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
High Risk Population ◽  
Short Term ◽  
Ambient Air Pollutants ◽  
Short Term Effects

In this study, we estimated the short-term effects of ambient air pollution on respiratory disease hospitalization in Taiyuan, China. Daily data of respiratory disease hospitalization, daily concentration of ambient air pollutants and meteorological factors from 1 October 2014 to 30 September 2017 in Taiyuan were included in our study. We conducted a time-series study design and applied a generalized additive model to evaluate the association between every 10-μg/m3 increment of air pollutants and percent increase of respiratory disease hospitalization. A total of 127,565 respiratory disease hospitalization cases were included in this study during the present period. In single-pollutant models, the effect values in multi-day lags were greater than those in single-day lags. PM2.5 at lag02 days, SO2 at lag03 days, PM10 and NO2 at lag05 days were observed to be strongly and significantly associated with respiratory disease hospitalization. No significant association was found between O3 and respiratory disease hospitalization. SO2 and NO2 were still significantly associated with hospitalization after adjusting for PM2.5 or PM10 into two-pollutant models. Females and younger population for respiratory disease were more vulnerable to air pollution than males and older groups. Therefore, some effective measures should be taken to strengthen the management of the ambient air pollutants, especially SO2 and NO2, and to enhance the protection of the high-risk population from air pollutants, thereby reducing the burden of respiratory disease caused by ambient air pollution.

scholarly journals Association between Ambient Air Pollutants and Preterm Birth in Ningbo, China: A Time-Series Study

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Zhe-Bin Yu ◽  
Wen-Yuan Liu ◽  
Hai-Yan Qiu ◽  
Die Li ◽  
Xue-Yu Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Preterm Birth ◽  
Air Pollutants ◽  
Ambient Air ◽  
Time Series Study ◽  
Ambient Air Pollutants ◽  
Series Study

Application of nonlinear land use regression models for ambient air pollutants and air quality index

2021 ◽  
Vol 12 (10) ◽  
pp. 101186
Author(s):  
Licheng Zhang ◽  
Xue Tian ◽  
Yuhan Zhao ◽  
Lulu Liu ◽  
Zhiwei Li ◽  
...  
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Air Pollutants ◽  
Regression Models ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Land Use Regression ◽  
Ambient Air Pollutants

scholarly journals A Literature Review on Prediction of Air Quality Index and Forecasting Ambient Air Pollutants using Machine Learning Algorithms

2020 ◽  
Vol 5 (8) ◽  
pp. 1148-1152
Author(s):  
Radhika M. Patil ◽  
Dr. H. T. Dinde ◽  
Sonali. K. Powar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Literature Review ◽  
Air Pollutants ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Ambient Air Pollutants

Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.

Abstract P067: Long-term Exposure to Ambient Air Pollution and Type 2 Diabetes Incidence: A Time Series Analysis

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Guozhang Xu ◽  
Donghuui Duan ◽  
Dingyun You ◽  
Jiaying Xu ◽  
Xiaoqi Feng ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Air Pollution ◽  
Time Series ◽  
Air Pollutants ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Relative Risks ◽  
Ambient Air Pollutants

Introduction: Epidemiological evidence on long-term exposure to ambient air pollution and type 2 diabetes (T2D) incidence are sparse, and the results are contradictory. Hypothesis: We performed a time-series analysis to investigate potential association between long-term exposure to ambient air pollution and T2D incidence in the Chinese population. Methods: Monthly time-series data between 2008-2015 on ambient air pollutants and incident T2D were obtained from the Environment Monitoring Center of Ningbo and the Chronic Disease Surveillance System of Ningbo. Relative risks (RRs) and 95% confidence intervals (95%CIs) of incident T2D per 10 μg/m 3 increase in ambient air pollutants were estimated from Poisson generalized additive models and adjusted for month, temperature, relative humidity, air pressure and wind speed. This model was combined with a distributed lag non-linear model to determine the relative risks. Main Outcome Measures: The main outcome measure was T2D incidence. Results: Long-term exposure to particulate matter <10 μm (PM10) and Sulphur dioxide (SO2) were associated with increased T2D incidence. The relative risks (RRs) of each increment in 10 μg/m 3 of PM10 and SO2 were 1.62 (95%CI, 1.16 to 2.28) and 1.63 (95%CI, 1.12 to 2.38) for overall participants, 1.56 (95%CI, 1.12 to 2.17) and 1.59 (95%CI, 1.14 to 2.23) for males, 1.68 (95%CI, 1.15 to 2.44) and 1.76 (95%CI, 1.21 to 2.56) for females, respectively. Whereas for ozone (O3) exposure, the RRs were 0.78 (95%CI, 0.68 to 0.90) for overall participants, 0.78 (95%CI, 0.69 to 0.90) for males, and 0.78 (95%CI, 0.67 to 0.91) for females, respectively. Female participants were more prone to develop T2D after long-term exposed to ambient air pollutants than male counterparts. No statistically significant associations were observed for PM2.5, NO2, and CO exposures, nor in the two- and three-pollutant models. Conclusions: Long-term exposure to PM10 and SO2 is positively associated with T2D incidence, whereas O3 is negatively associated with T2D incidence.

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