scholarly journals Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

2020 ◽  
Vol 20 (6) ◽  
pp. 1429-1439 ◽  
Author(s):  
Qingchun Guo ◽  
Zhenfang He ◽  
Shanshan Li ◽  
Xinzhou Li ◽  
Jingjing Meng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Meteorological Conditions ◽  
Wavelet Neural Networks ◽  
Air Pollution Forecasting

An online air pollution forecasting system using neural networks

2008 ◽  
Vol 34 (5) ◽  
pp. 592-598 ◽  
Author(s):  
Atakan Kurt ◽  
Betul Gulbagci ◽  
Ferhat Karaca ◽  
Omar Alagha
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Forecasting System ◽  
Air Pollution Forecasting

Recurrent Neural Networks for Analysis and Automated Air Pollution Forecasting

2019 ◽  
pp. 50-59 ◽  
Author(s):  
Ching-Fang Lee ◽  
Chao-Tung Yang ◽  
Endah Kristiani ◽  
Yu-Tse Tsan ◽  
Wei-Cheng Chan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Recurrent Neural Networks ◽  
Air Pollution Forecasting

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 Meteorological Variables and Synoptic Patterns Associated with Air Pollutions in Eastern China during 2013–2018

2020 ◽  
Vol 17 (7) ◽  
pp. 2528 ◽  
Author(s):  
Zhujun Dai ◽  
Duanyang Liu ◽  
Kun Yu ◽  
Lu Cao ◽  
Youshan Jiang
Keyword(s):  
Air Pollution ◽  
Eastern China ◽  
Meteorological Condition ◽  
Temperature Inversion ◽  
Meteorological Conditions ◽  
Meteorological Variables ◽  
Synoptic Situation ◽  
Factors Affecting ◽  
Air Pollutions ◽  
Meteorological Elements

Steady meteorological conditions are important external factors affecting air pollution. In order to analyze how adverse meteorological variables affect air pollution, surface synoptic situation patterns and meteorological conditions during heavy pollution episodes are discussed. The results showed that there were 78 RPHPDs (regional PM2.5 pollution days) in Jiangsu, with a decreasing trend year by year. Winter had the most stable meteorological conditions, thus most RPHPDs appeared in winter, followed by autumn and summer, with the least days in spring. RPHPDs were classified into three patterns, respectively, as equalized pressure (EQP), advancing edge of a cold front (ACF) and inverted trough of low pressure (INT) according to the SLP (sea level pressure). RPHPDs under EQP were the most (51%), followed by ACF (37%); INT was the minimum (12%). Using statistical methods and meteorological condition data on RPHPDs from 2013 to 2017 to deduce the thresholds and 2018 as an independent dataset to validate the proposed thresholds, the threshold values of meteorological elements are summarized as follows. The probability of RPHPDs without rain was above 92% with the daily and hourly precipitation of all RPHPDs below 2.1 mm and 0.8 mm. Wind speed, RHs, inversion intensity(ITI), height difference in the temperature inversion(ITK), the lower height of temperature inversion (LHTI) and mixed-layer height (MLH) in terms of 25%–75% high probability range were respectively within 0.5–3.6 m s−1, 55%–92%, 0.7–4.0 °C 100 m −1, 42–576 m, 3–570 m, 200–1200 m. Two conditions should be considered: whether the pattern was EQP, ACF or INT and whether the eight meteorological elements are within the thresholds. If both criteria are met, PM2.5 particles tend to accumulate and air pollution diffusion conditions are poor. Unfavorable meteorological conditions are the necessary, but not sufficient condition for RPHPDs.

Sign in / Sign up

Export Citation Format

Share Document