Observations and simulations with linked numerical PBL and air pollution models and a Gaussian model under low wind speed conditions: a case study of the urban-industrial Linz area

1990 ◽  
Vol 13 (6) ◽  
pp. 903-915 ◽  
Author(s):  
U. Pechinger ◽  
P. Seibert
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Gaussian Model ◽  
Low Wind Speed ◽  
Air Pollution Models

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint)

2020 ◽  
Author(s):  
Mario Coccia
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Critical Role ◽  
Meteorological Condition ◽  
Meteorological Conditions ◽  
Lessons Learned ◽  
Transmission Dynamics ◽  
Critical Threshold ◽  
Viral Infectivity ◽  
Low Wind Speed

BACKGROUND Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. OBJECTIVE This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society. METHODS Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020. RESULTS The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. CONCLUSIONS Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. CLINICALTRIAL not applicable

scholarly journals Measurement report: Effect of wind shear on PM<sub>10</sub> concentration vertical structure in urban boundary layer in a complex terrain

2021 ◽  
Author(s):  
Piotr Sekuła ◽  
Anita Bokwa ◽  
Jakub Bartyzel ◽  
Bogdan Bochenek ◽  
Łukasz Chmura ◽  
...  
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Case Studies ◽  
Vertical Profile ◽  
Wind Shear ◽  
Transitional Zone ◽  
Model Data ◽  
Profile Modification ◽  
Low Wind Speed ◽  
Clean Air

Abstract. The paper shows wind shear impact on PM10 vertical profiles, in Kraków, southern Poland. The data used consist of background data for two cold seasons (Sep. 2018 to Apr. 2019, and Sep. 2019 to Apr. 2020), and data for several case studies from November 2019 to March 2020. The data is composed of PM10 measurements, model data, and wind speed and direction data. The background model data come from operational forecast results of AROME model. PM10 concentration in the vertical profile was measured with a sightseeing balloon. Significant spatial variability of wind field was found. The case studies represent the conditions with much lower wind speed and a much higher PM10 levels than the seasonal average. The inversions were much more frequent than on average, too. Wind shear turned out to be the most important factor in terms of PM10 vertical profile modification. It is generated due to the relief impact, i.e. the presence of a large valley, blocked on one side with the hills. The analysis of PM10 profiles from all flights allows to distinguish three vertical zones of potential air pollution hazard within the valley (about 100 m deep) and the city of Kraków: 1. up to about 60 m a.g.l. – the zone where during periods of low wind speed, air pollution is potentially the highest and the duration of such high levels is the longest, i.e. the zone with the worst aerosanitary conditions; 2. about 60–100 m a.g.l. – transitional zone where the large decrease of PM10 levels with height is observed; 3. above 100–120 m a.g.l. – the zone where air quality is significantly better than in the zone 1, either due to the increase of the wind speed, or due to the wind direction change and advection of different, clean air masses.

Performance evaluation of modified Gaussian and Lagrangian models under low wind speed: A case study

2019 ◽  
Vol 133 ◽  
pp. 562-567 ◽  
Author(s):  
P.T. Rakesh ◽  
R. Venkatesan ◽  
C.V. Srinivas ◽  
R. Baskaran ◽  
B. Venkatraman
Keyword(s):  
Performance Evaluation ◽  
Wind Speed ◽  
Lagrangian Models ◽  
Low Wind Speed

scholarly journals Time Series Modeling of Air Pollution and Its Association with Season and Climate Variables in Istanbul, Turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

A methodology for improving wind energy production in low wind speed regions, with a case study application in Iraq

2019 ◽  
Vol 127 ◽  
pp. 89-102 ◽  
Author(s):  
Abdul Salam Darwish ◽  
Sabry Shaaban ◽  
Erika Marsillac ◽  
Nazar Muneam Mahmood
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Production ◽  
Low Wind Speed ◽  
Study Application

On the variability of the wind speed exponent in urban air pollution models

1988 ◽  
Vol 22 (9) ◽  
pp. 2013-2019 ◽  
Author(s):  
A.J. Jakeman ◽  
Bai Jun ◽  
J.A. Taylor
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Urban Air Pollution ◽  
Urban Air ◽  
Air Pollution Models
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