One-way coupling of WRF with a Gaussian dispersion model: a focused fine-scale air pollution assessment on southern Mediterranean

Yue

et al. 2020

Environ. Sci. Technol.

Self Cite

107

The Corona Virus Disease 2019 (COVID-19) is rapidly spreading throughout the world. Aerosol is a potential transmission route. We conducted the quantitative microbial risk assessment (QMRA) to evaluate the aerosol transmission risk by using the South China Seafood Market as an example. The key processes were integrated, including viral shedding, dispersion, deposition in air, biologic decay, lung deposition, and the infection risk based on the dose–response model. The available hospital bed for COVID-19 treatment per capita (1.17 × 10–3) in Wuhan was adopted as a reference for manageable risk. The median risk of a customer to acquire SARS-CoV-2 infection via the aerosol route after 1 h of exposure in the market with one infected shopkeeper was about 2.23 × 10–5 (95% confidence interval: 1.90 × 10–6 to 2.34 × 10–4). The upper bound could increase and become close to the manageable risk with multiple infected shopkeepers. More detailed risk assessment should be conducted in poorly ventilated markets with multiple infected cases. The uncertainties were mainly due to the limited information on the dose–response relation and the viral shedding which need further studies. The risk rapidly decreased outside the market due to the dilution by ambient air and became below 10–6 at 5 m away from the exit.

Section: Introductionmentioning

confidence: 99%

Infection Risk Assessment of COVID-19 through Aerosol Transmission: a Case Study of South China Seafood Market

Yue

et al. 2020

Environ. Sci. Technol.

Self Cite

107

“…The aforementioned studies provide direct observations of the aviation impacts, but further studies are needed for a comprehensive assessment of the whole area around the airport for an extended time span. Air quality models are currently the most practical way to evaluate the effects of pollution emissions. − The global or regional air quality models have already been applied to assess the mass-based impacts of aircraft particulate matter emission. ,− However, there is scarce particle number-based assessment due to the limited emission data and the difficulties in modeling the number-based aerosol dynamics…”

Section: Introductionmentioning

confidence: 99%

Influence of Aviation Emission on the Particle Number Concentration near Zurich Airport

Karl

et al. 2020

Environ. Sci. Technol.

Self Cite

In addition to the much-publicized environmental impact of CO2 emission by air traffic, aviation particulate emission also deserves attention. The abundant ultrafine particles in the aviation exhaust with diameters less than 100 nm may penetrate deep into the human respiratory system and cause adverse health effects. Here, we quantified the detailed aviation particle number emission from Zurich Airport and evaluated its influences on the annual mean particle number concentrations in the surrounding communities. The actual flight trajectory data were utilized for the first time to develop an emission inventory with high spatial resolution. The estimated total particle number emission was in the magnitude of 1024 particles per year. The annual mean particle mass concentrations in the nearby communities were increased by about 0.1 μg m–3 due to the aviation emission, equivalent to about 1% of the background concentration. However, the particle number concentration could be increased by a factor of 2–10 of the background level (104 cm–3) for nearby communities. Further studies are required to investigate the health effects of the increased particle number concentration and to evaluate whether the regulation based on the mass concentration is still sufficient for the air quality near airports.

“…Statistical models, including linear regression and an autoregressive integrated moving average (Hu et al, 2021), prediction consistent with earlier observations. Notable applications include: (Snoun et al, 2019) utilized the Gaussian atmospheric model to accurately retrieve the short-range distribution of wind speed. (Natarajan et al, 2021) compared probability distribution models for performance monitoring in Indian wind farms.…”

Section: Related Workmentioning

confidence: 99%

Weather forecasting based on data-driven and physics-informed reservoir computing models

Mammedov

Olugu

Farah

2021

Environ Sci Pollut Res

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.