scholarly journals Exposure to Air Pollution and Particle Radioactivity With the Risk of Ventricular Arrhythmias

Circulation ◽  
2020 ◽  
Vol 142 (9) ◽  
pp. 858-867 ◽  
Author(s):  
Adjani A. Peralta ◽  
Mark S. Link ◽  
Joel Schwartz ◽  
Heike Luttmann-Gibson ◽  
Douglas W. Dockery ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Ventricular Arrhythmias ◽  
Moving Average ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Interquartile Range ◽  
Dew Point ◽  
High Risk Population ◽  
Air Temperatures

Background: Individuals are exposed to air pollution and ionizing radiation from natural sources through inhalation of particles. This study investigates the association between cardiac arrhythmias and short-term exposures to fine particulate matter (particulate matter ≤2.5 µm aerodynamic diameter; PM 2.5 ) and particle radioactivity. Methods: Ventricular arrhythmic events were identified among 176 patients with dual-chamber implanted cardioverter-defibrillators in Boston, Massachusetts between September 2006 and June 2010. Patients were assigned exposures based on residential addresses. Daily PM 2.5 levels were estimated at 1-km×1-km grid cells from a previously validated prediction model. Particle gross β activity was used as a surrogate for particle radioactivity and was measured from several monitoring sites by the US Environmental Protection Agency’s monitoring network. The association of the onset of ventricular arrhythmias (VA) with 0- to 21-day moving averages of PM 2.5 and particle radioactivity (2 single-pollutant models and a 2-pollutant model) before the event was examined using time-stratified case-crossover analyses, adjusted for dew point and air temperatures. Results: A total of 1,050 VA were recorded among 91 patients, including 123 sustained VA among 25 of these patients. In the single-pollutant model of PM 2.5 , each interquartile range increase in daily PM 2.5 levels for a 21-day moving average was associated with 39% higher odds of a VA event (95% CI, 12%–72%). In the single-pollutant model of particle radioactivity, each interquartile range increase in particle radioactivity for a 2-day moving average was associated with 13% higher odds of a VA event (95% CI, 1%–26%). In the 2-pollutant model, for the same averaging window of 21 days, each interquartile range increase in daily PM 2.5 was associated with an 48% higher odds of a VA event (95% CI, 15%–90%), and each interquartile range increase of particle radioactivity with a 10% lower odds of a VA event (95% CI, –29% to 14%). We found that with higher levels of particle radioactivity, the effect of PM 2.5 on VAs is reduced. Conclusions: In this high-risk population, intermediate (21-day) PM 2.5 exposure was associated with higher odds of a VA event onset among patients with known cardiac disease and indication for implanted cardioverter-defibrillator implantation independently of particle radioactivity.

Time Series Analysis for Prediction of PM2.5 Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model on Taiwan Air Quality Monitoring Network Data

2020 ◽  
Vol 17 (9) ◽  
pp. 3964-3969
Author(s):  
Doreswamy ◽  
K. S. Harish Kumar ◽  
Ibrahim Gad
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Moving Average ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Data Set ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average

Nowadays, in Taiwan, due to the increasing number of vehicles, industrialization of large energy consumption, uncontrolled constructions and urbanization, air pollution is becoming a major problem. Hence, it is necessary to control air pollution by applying air quality monitoring actions. The particulate matter (PM2.5) of the air pollution in TAQMN data is the main pollutant accountable for at least two-thirds of the severely polluted days in the major cities of Taiwan. In this work, machine learning (ML) techniques are widely used in developing models that can be used to control the air pollution. Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used to predict the air pollution concentration, where the dataset chronologically from 2012 to 2016 are used to train the proposed method and testing data set from 2016 to 2017. The result of the SARIMA model shows high precision in forecasting the future values of particulate matter (P2.5) level with minimum error.

Chinese emissions reductions deliver reduced PM2.5-caused mortality across China during 2015-2017

2020 ◽  
Author(s):  
Ben Silver ◽  
Luke Conibear ◽  
Carly Reddington ◽  
Christophe Knote ◽  
Steve Arnold ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Minor Role ◽  
Chinese Government ◽  
Ozone Concentrations ◽  
National Monitoring ◽  
A Minor

<p>Air pollution is a serious environmental issue and leading contributor to the disease burden in China. Following severe air pollution episodes during the 2012-2013 winter, the Chinese government has prioritised efforts to reduce PM<sub>2.5</sub> emissions, and established a national monitoring network to record air quality trends. Rapid reductions in fine particulate matter (PM<sub>2.5</sub>) concentrations and increased ozone concentrations have occurred across China, during 2015 to 2017. We used measurements of particulate matter with a diameter < 2.5 µm (PM<sub>2.5</sub>) and Ozone (O<sub>3</sub>) from >1000 stations across China combined with similar datasets from Hong Kong and Taiwan to calculate trends in PM<sub>2.5</sub>, Nitrogen Dioxide, Sulphur Dioxide and O<sub>3</sub> across the greater China region during 2015-2019. We then use the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) regional air quality simulations, to explore the drivers and impacts of observed trends. Using annually varying emissions from the Multi-resolution Emission Inventory for China, we simulate air quality across China during 2015-2017, and calculate a median PM<sub>2.5</sub> trends of -3.9 µg m<sup>-3</sup> year<sup>-1</sup>. The measured nationwide median PM<sub>2.5</sub> trend of -3.4 µg m<sup>-3</sup> year<sup>-</sup>. With anthropogenic emissions fixed at 2015-levels, the simulated trend was much weaker (-0.6 µg m<sup>-3</sup> year<sup>-1</sup>), demonstrating interannual variability in meteorology played a minor role in the observed PM<sub>2.5</sub> trend. The model simulated increased ozone concentrations in line with the measurements, but underestimated the magnitude of the observed absolute trend by a factor of 2. We combined simulated trends in PM<sub>2.5</sub> concentrations with an exposure-response function to estimate that reductions in PM<sub>2.5</sub> concentrations over this period have reduced PM<sub>2.5</sub>-attribrutable premature morality across China by 150 000 deaths year<sup>-1</sup>.</p>

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

scholarly journals Long‐Term Exposure to Particulate Air Pollution Is Associated With 30‐Day Readmissions and Hospital Visits Among Patients With Heart Failure

2021 ◽  
Author(s):  
Cavin K. Ward‐Caviness, ◽  
Mahdieh Danesh Yazdi, ◽  
Joshua Moyer, ◽  
Anne M. Weaver, ◽  
Wayne E. Cascio, ◽  
...  
Keyword(s):  
Heart Failure ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Total Hospital ◽  
Fine Particulate ◽  
Patients With Heart Failure ◽  
Hospital Visits ◽  
Pollution Exposure

Background Long‐term air pollution exposure is a significant risk factor for inpatient hospital admissions in the general population. However, we lack information on whether long‐term air pollution exposure is a risk factor for hospital readmissions, particularly in individuals with elevated readmission rates. Methods and Results We determined the number of readmissions and total hospital visits (outpatient visits+emergency room visits+inpatient admissions) for 20 920 individuals with heart failure. We used quasi‐Poisson regression models to associate annual average fine particulate matter at the date of heart failure diagnosis with the number of hospital visits and 30‐day readmissions. We used inverse probability weights to balance the distribution of confounders and adjust for the competing risk of death. Models were adjusted for age, race, sex, smoking status, urbanicity, year of diagnosis, short‐term fine particulate matter exposure, comorbid disease, and socioeconomic status. A 1‐µg/m 3 increase in fine particulate matter was associated with a 9.31% increase (95% CI, 7.85%–10.8%) in total hospital visits, a 4.35% increase (95% CI, 1.12%–7.68%) in inpatient admissions, and a 14.2% increase (95% CI, 8.41%–20.2%) in 30‐day readmissions. Associations were robust to different modeling approaches. Conclusions These results highlight the potential for air pollution to play a role in hospital use, particularly hospital visits and readmissions. Given the elevated frequency of hospitalizations and readmissions among patients with heart failure, these results also represent an important insight into modifiable environmental risk factors that may improve outcomes and reduce hospital use among patients with heart failure.

scholarly journals Assessment of PM10 in Aurangabad City of Central India

2016 ◽  
Vol 5 (2) ◽  
pp. 61-74 ◽  
Author(s):  
Geetanjali Kaushik ◽  
Arvind Chel ◽  
Sangeeta Shinde ◽  
Ashish Gadekar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Tall Buildings ◽  
Central India ◽  
Air Pollutant ◽  
Traffic Density ◽  
High Pollution ◽  
High Concentrations ◽  
The City

Almost 670 million people comprising 54.5% of our population reside in regions that do not meet the Indian NAAQS for fine particulate matter. Numerous studies have revealed a consistent correlation for particulate matter concentration with health than any other air pollutant. Aurangabad city a rapidly growing city with population of 1.5 million is home to five major industrial areas, the city is also known for its historical monuments which might also be adversely affected from air pollution. Therefore, this research aims at estimating PM10 concentrations at several locations across Aurangabad. The concentration of PM10 was highest at the Railway Station followed by Waluj (an industrial zone) and City chowk is the centre of the city which has high population, tall buildings, few open spaces which causes high congestion and does not allow the particulates to disperse. Other locations with high concentrations of PM are Mill corner, Harsul T-point, Kranti Chowk, Seven Hill, TV centre and Beed Bye pass. All these locations have narrow roads, high traffic density, poor road condition with pot holes and few crossing points which cause congestion and vehicle idling which are responsible for high pollution. Therefore, it is evident that air pollution is a serious issue in the city which may be further aggravated if it is not brought under control. Hence, strategies have to be adopted for combating the menace of air pollution.INTERNATIONAL JOURNAL OF ENVIRONMENTVolume-5, Issue-2, March-May 2016, Page :61-74

scholarly journals Visualization of Correlation for Air Pollution Time Series using TAQMN Data

2019 ◽  
Vol 8 (3) ◽  
pp. 7922-7927
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Particulate Matter ◽  
Pearson Correlation ◽  
Fine Particulate Matter ◽  
Correlation Method ◽  
Well Being ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Pm2.5 Concentration

In Taiwan country Annan, Chiayi, Giran, and Puzi cities are facing a serious fine particulate matter (PM2.5) issue. To date the impressive advance has been made toward understanding the PM2.5 issue, counting special temporal characterization, driving variables and well-being impacted. However, notable research as has been done on the interaction of the content between the selected cities of Taiwan country for particulate matter (PM2.5) concentration. In this paper, we purposed a visualization technique based on this principle of the visualization, cross-correlation method and also the time-series concentration with particulate matter (PM2.5) for different cities in Taiwan. The visualization also shows that the correlation between the different meteorological factors as well as the different air pollution pollutants for particular cities in Taiwan. This visualization approach helps to determine the concentration of the air pollution levels in different cities and also determine the Pearson correlation, r values of selected cities are Annan, Puzi, Giran, and Wugu.

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