scholarly journals Positive correlation between long term emission of several air pollutants and COVID-19 deaths in Sweden

2020 ◽  
Author(s):  
Lars Helander
Keyword(s):  
Air Quality ◽  
Population Density ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Threshold Value ◽  
Wind Speeds ◽  
Average Wind ◽  
Incidence And Mortality ◽  
Positive Correlations

AbstractSeveral recent studies have found troubling links between air pollution and both incidence and mortality of COVID-19, the pandemic disease caused by the virus SARS-CoV-2. Here, we investigate whether such a link can be found also in Sweden, a country with low population density and a relatively good air quality in general, with low background levels of important pollutants such as PM2.5 and NO2. The investigation is carried out by relating normalized emission levels of several air pollutants to normalized COVID-19 deaths at the municipality level, after applying a sieve function using an empirically determined threshold value to filter out noise. We find a fairly strong correlation for PM2.5, PM10 and SO2, and a moderate one for NOx. We find no correlation neither for CO, nor (as expected) for CO2. Our results are statistically significant and the calculations are simple and easily verifiable. Since the study considers only emission levels of air pollutants and not measurements of air quality, climatic and meteorological factors (such as average wind speeds) can trivially be ruled out as confounders. Finally, we also show that although there are small positive correlations between population density and COVID-19 deaths in the studied municipalities (which are for the most part rural and non densely populated) they are either weak or not statistically significant.

scholarly journals Long-Term Exposure to Fine and Coarse Particulate Matter and COVID-19 Incidence and Mortality Rate in Chile during 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7409
Author(s):  
Macarena Valdés Salgado ◽  
Pamela Smith ◽  
Mariel Opazo ◽  
Nicolás Huneeus
Keyword(s):  
Mortality Rate ◽  
Incidence Rate ◽  
Air Pollutants ◽  
Negative Binomial ◽  
Transport Model ◽  
Surface Concentration ◽  
Mortality Rates ◽  
Incidence And Mortality ◽  
Pm2.5 And Pm10

Background: Several countries have documented the relationship between long-term exposure to air pollutants and epidemiological indicators of the COVID-19 pandemic, such as incidence and mortality. This study aims to explore the association between air pollutants, such as PM2.5 and PM10, and the incidence and mortality rates of COVID-19 during 2020. Methods: The incidence and mortality rates were estimated using the COVID-19 cases and deaths from the Chilean Ministry of Science, and the population size was obtained from the Chilean Institute of Statistics. A chemistry transport model was used to estimate the annual mean surface concentration of PM2.5 and PM10 in a period before the current pandemic. Negative binomial regressions were used to associate the epidemiological information with pollutant concentrations while considering demographic and social confounders. Results: For each microgram per cubic meter, the incidence rate increased by 1.3% regarding PM2.5 and 0.9% regarding PM10. There was no statistically significant relationship between the COVID-19 mortality rate and PM2.5 or PM10. Conclusions: The adjusted regression models showed that the COVID-19 incidence rate was significantly associated with chronic exposure to PM2.5 and PM10, even after adjusting for other variables.

scholarly journals Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

2017 ◽  
Vol 17 (11) ◽  
pp. 7261-7276 ◽  
Author(s):  
Tobias Wolf-Grosse ◽  
Igor Esau ◽  
Joachim Reuder
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Large Eddy Simulation ◽  
Air Pollutants ◽  
Large Scale ◽  
Wind Speeds ◽  
Street Level ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Air Pockets

Abstract. Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s−1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES) experiments with the Parallelised Large-Eddy Simulation Model (PALM) for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a relatively small local water body acted as a barrier for the horizontal transport of air pollutants from the largest street in the valley and along the valley bottom, transporting them vertically instead and hence diluting them. We found that the stable stratification accumulates the street-level pollution from the transport corridor in shallow air pockets near the surface. The polluted air pockets are transported by the local recirculations to other less polluted areas with only slow dilution. This combination of relatively long distance and complex transport paths together with weak dispersion is not sufficiently resolved in classical air pollution models. The findings have important implications for the air quality predictions over urban areas. Any prediction not resolving these, or similar local dynamic features, might not be able to correctly simulate the dispersion of pollutants in cities.

Height Extrapolation of Wind Data

1985 ◽  
Vol 107 (1) ◽  
pp. 10-14 ◽  
Author(s):  
A. S. Mikhail
Keyword(s):  
Wind Speed ◽  
Power Law ◽  
Wind Data ◽  
Power Law Model ◽  
Short Term ◽  
Shape Factors ◽  
Wind Speeds ◽  
Average Wind ◽  
Modified Power Law

Various models that are used for height extrapolation of short and long-term averaged wind speeds are discussed. Hourly averaged data from three tall meteorological towers (the NOAA Erie Tower in Colorado, the Battelle Goodnoe Hills Tower in Washington, and the WKY-TV Tower in Oklahoma), together with data from 17 candidate sites (selected for possible installation of large WECS), were used to analyze the variability of short-term average wind shear with atmospheric and surface parameters and the variability of the long-term Weibull distribution parameter with height. The exponents of a power-law model, fit to the wind speed profiles at the three meteorological towers, showed the same variability with anemometer level wind speed, stability, and surface roughness as the similarity law model. Of the four models representing short-term wind data extrapolation with height (1/7 power law, logarithmic law, power law, and modified power law), the modified power law gives the minimum rms for all candidate sites for short-term average wind speeds and the mean cube of the speed. The modified power-law model was also able to predict the upper-level scale factor for the WKY-TV and Goodnoe Hills Tower data with greater accuracy. All models were not successful in extrapolation of the Weibull shape factors.

scholarly journals Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

2018 ◽  
Author(s):  
Laban N. Ongaki ◽  
Christopher M. Maghanga ◽  
Joash Kerongo
Keyword(s):  
Wind Speed ◽  
Rayleigh Distribution ◽  
Series Data ◽  
Energy Potential ◽  
Distribution Models ◽  
Wind Speeds ◽  
Average Wind ◽  
Mean Wind Speed ◽  
Wind Energy Potential

The research sought to investigate the long term characteristics of wind in the Kisii region (elevation 1710m above sea level, 0.68oS, 34.79o E). Wind speeds were analyzed and characterized on short term (per month for a year) and then simulated for long term (ten years) measured hourly series data of daily wind speeds at a height of 10m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent; the mean wind speed, diurnal variations, daily variations as well as the monthly variations. The wind speed frequency distribution at the height 10 m was found to be 2.9ms-1 with a standard deviation of 1.5. Based on the two month’s data that was extracted from the AcuRite 01024 Wireless Weather Stations with 5-in-1 Weather Sensor experiments set at three sites in the region, averages of wind speeds at hub heights of 10m and 13m were calculated and found to be 1.7m/s, 2.0m/s for Ikobe station, 2.4m/s, 2.8m/s for Kisii University stations, and 1.3m/s, 1.6m/s for Nyamecheo station respectively. Then extrapolation was done to determine average wind speeds at heights (20m, 30m, 50m, and 70m) which were found to be 85.55W/m2, 181.75W/m2, 470.4W/m2 and 879.9W/m2 respectively. The wind speed data was used statistically to model a Weibull probability density function and used to determine the power density for Kisii region.

scholarly journals Measurement strategies for estimating long-term average wind speeds

1980 ◽  
Author(s):  
J.V. Ramsdell ◽  
S. Houston ◽  
H.L. Wegley
Keyword(s):  
Wind Speeds ◽  
Average Wind ◽  
Measurement Strategies

scholarly journals Daily variability of air pollution in locations of different population density

2019 ◽  
Vol 100 ◽  
pp. 00011
Author(s):  
Robert Cichowicz ◽  
Artur Stelęgowski
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Population Density ◽  
Air Pollutants ◽  
Daily Maximum ◽  
Air Contaminants ◽  
Daily Variability ◽  
Air Quality Monitoring Stations ◽  
Agricultural Areas ◽  
Daily Changes

The air quality levels vary during a day, especially in inhabited areas. Therefore, it seems reasonable to observe and analyze the occurrence of daily maximum and minimum level of air pollution. In this article, data obtained from automatic air quality monitoring stations located in 5 large, 5 small and medium cities and 5 villages in Poland was analyzed in 2012−2016. Those locations vary, inter alia, depending on number of inhabitants and population density, and for this reason also due to the presence of air contaminants. As an indicator of daily variability air pollution it was determined the ratio of maximum to minimum concentrations of selected air pollutants (NO2 and NOx, and O3, SO2, CO, PM10 and PM2.5, and benzene) in urban and agricultural areas. In winter, the daily changes were bigger in cities than in villages. While in summer, the level of daily variability was similar, irrespective of size of the settlement unit. The biggest daily changes concerned nitrogen oxides, the lowest − sulfur dioxide and dusts.

Abstract P002: Long Term Exposure to Outdoor Air Pollution and Cardiovascular Health

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Sarah Singh ◽  
Courtney Pilkerton ◽  
Adam Christian ◽  
Thomas K Bias ◽  
Stephanie J Frisbee
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Air Pollutants ◽  
Cardiovascular Health ◽  
Heart Association ◽  
Health Concern ◽  
Behavioral Risk ◽  
The Individual

BACKGROUND: Although the link between air pollution and cardiovascular disease has been controversial in recent decades, it remains a top global health concern. Most studies have assessed only the relationship between pollutant concentrations and morbidity or mortality in populous cities. In this study, we investigated the association of long term exposure to major air pollutants with current cardiovascular health. This outcome was a measure of health rather than disease, as measured by the Cardiovascular Health Index (CVHI) developed by the American Heart Association. METHODS: We analyzed 2011 data from 3007 counties across the US using Behavioral Risk Factor Surveillance System and Area Health Resources File. Air Quality Index (AQI) for five major pollutants from 2001-2011; Ozone, Sulfur dioxide and Carbon monoxide and Fine particulate matter (aerodynamic diameter of 10 and ≤2.5 μm) were obtained from the EPA Air Quality System database. Categories were based on the 11-year average pollutant AQI level and using Jenks optimization method; persistently good, variant and persistently bad. Associations between categories and the mean CVHI were evaluated using Poisson regression models adjusting for age, sex, race/ethnicity and socioeconomic status at the individual and population level. RESULTS: PM2.5 was most frequently measured (938 counties) and carbon monoxide least frequently (224 counties). Correlations between pollutants were moderate and significant (p<0.0001), ranging from r=0.30 between CO and Oz to r=0.52 between SD and PM2.5. Four pollutants had 11-year average AQI levels significantly associated with increased mean CVHI score of individuals. Living in a county categorized as ‘persistently good’ or ‘variant’ AQI levels for ozone is significantly associated with an estimated 3% increase in CVHI (95% CI 0.1% - 5.0%) as compared to living in a county of ‘persistently bad’ AQI levels. In addition, living in a county of only ‘persistently good’ AQI levels for PM2.5 is significantly associated with an estimated 5% increase in CVHI (95% CI 3% - 9%) as compared to living in a county of ‘persistently bad’ AQI levels. Inverse relationships existed for both PM10 and carbon monoxide. CONCLUSIONS: It is difficult to tease apart the independent effects of individual air pollutants on health as humans are exposed to a mixture of gases. However we have shown that at the individual level, there is an association between long term exposure to air pollution and its effects on current cardiovascular health. Further research is needed to determine whether these effects exist at varying levels of subject characteristics.

scholarly journals Long-Term Atmospheric Visibility Trends and Characteristics of 31 Provincial Capital Cities in China during 1957–2016

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 318 ◽  
Author(s):  
Weicong Fu ◽  
Ziru Chen ◽  
Zhipeng Zhu ◽  
Qunyue Liu ◽  
Jinda Qi ◽  
...  
Keyword(s):  
Air Quality ◽  
Socioeconomic Factors ◽  
Urban Areas ◽  
Meteorological Factors ◽  
Ambient Air ◽  
Ambient Air Quality ◽  
Capital Cities ◽  
The Public ◽  
Provincial Capital

Millions of pulmonary diseases, respiratory diseases, and premature deaths are caused by poor ambient air quality in developing countries, especially in China. A proven indicator of ambient air quality, atmospheric visibility (AV), has displayed continuous decline in China’s urban areas. A better understanding of the characteristics and the factors affecting AV can help the public and policy makers manage their life and work. In this study, long-term AV trends (from 1957–2016, excluding 1965–1972) and spatial characteristics of 31 provincial capital cities (PCCs) of China (excluding Taipei, Hong Kong, and Macau) were investigated. Seasonal and annual mean values of AV, percentage of ‘good’ (≥20 km) and ‘bad’ AV (<10 km), cumulative percentiles and the correlation between AV, socioeconomic factors, air pollutants and meteorological factors were analyzed in this study. Results showed that annual mean AV of the 31 PCCs in China were 14.30 km, with a declining rate of −1.07 km/decade. The AV of the 31 PCCs declined dramatically between 1973–1986, then plateaued between 1987–2006, and rebounded slightly after 2007. Correlation analysis showed that impact factors (e.g., urban size, industrial activities, residents’ activities, urban greening, air quality, and meteorological factors) contributed to the variation of AV. We also reveal that residents’ activities are the primary direct socioeconomic factors on AV. This study hopes to help the public fully understand the characteristics of AV and make recommendations about improving the air environment in China’s urban areas.

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