scholarly journals Approximation Formula for the Prediction of Downwind Distance that Found the Maximum Ground Level Concentration of air Pollution Based on the Gaussian Model

2015 ◽  
Vol 197 ◽  
pp. 1257-1262 ◽  
Author(s):  
Ronbanchob Apiratikul
Author(s):  
R. J. Ketterer ◽  
N. R. Dibelius

This paper summarizes regulations from 80 countries covering air pollution emissions from gas turbines. The paper includes emission and ground level concentration standards for particulates, sulfur dioxide, oxides of nitrogen, visible emissions, and carbon monoxide.


Environments ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 2
Author(s):  
Peter Brimblecombe ◽  
Yonghang Lai

The COVID-19 pandemic made it critical to limit the spread of the disease by enforcing human isolation, restricting travel and reducing social activities. Dramatic improvements to air quality, especially NO2, have often characterised places under COVID-19 restrictions. Air pollution measurements in Sydney in April 2019 and during the lockdown period in April 2020 show reduced daily averaged NO2 concentrations: 8.52 ± 1.92 and 7.85 ± 2.92 ppb, though not significantly so (p1~0.15) and PM2.5 8.91 ± 4.94 and 7.95 ± 2.64 µg m−3, again a non-significant difference (p1~0.18). Satellite imagery suggests changes that parallel those at ground level, but the column densities averaged over space and time, in false-colour, are more dramatic. Changed human mobility could be traced in increasing times spent at home, assessed from Google Mobility Reports and mirrored in decreased traffic flow on a major road, suggesting compliance with the restrictions. Electricity demand for the State of New South Wales was low under lockdown in early April 2020, but it recovered rapidly. Analysis of the uses of search terms: bushfires, air quality, haze and air pollution using Google Trends showed strong links between bushfires and pollution-related terms. The smoke from bushfires in late 2019 may well have added to the general impression of improved air quality during lockdown, despite only modest changes in the ground level measurements. This gives hints that successful regulation of air quality requires maintaining a delicate balance between our social perceptions and the physical reality.


Author(s):  
Anthony Vipin Das ◽  
Sayan Basu

The aim of this study was to describe the correlation between the meteorological and air pollution parameters with the temporal pattern of presentation of recent onset allergic eye disease (AED). This cross-sectional hospital-based study included new patients (≤21 years of age) presenting between January 2016 and August 2018 from the district of Hyderabad with a clinical diagnosis of AED and an acute exacerbation of recent onset of symptoms of less than 3 months duration. Correlation analysis was performed with the local environmental rainfall, temperature, humidity, windspeed, and air pollution. Of the 25,354 new patients hailing from the district of Hyderabad, 2494 (9.84%) patients were diagnosed with AED, of which 1062 (4.19%) patients had recent onset of symptoms. The mean monthly prevalence in this cohort was 4.13%, and the month of May (6.09%) showed the highest levels. The environmental parameters of humidity (r2 = 0.83/p = < 0.0001) and rainfall (r2 = 0.41/p = 0.0232) showed significant negative correlation, while temperature (r2 = 0.43/p = 0.0206) and ground-level ozone (r2 = 0.41/p = 0.0005) showed significant positive correlation with the temporal pattern of AED in the population. An increase in rainfall and humidity was associated with a lower prevalence, and an increase of temperature and ground-level ozone was associated with a higher prevalence of AED cases during the year among children and adolescents.


2020 ◽  
Vol 4 (1) ◽  
pp. 17
Author(s):  
Saisantosh Vamshi Harsha Madiraju ◽  
Ashok Kumar

Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. The sensitivity of the model to compute ground level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables. However, the model equations should be re-examined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.


Atmosphere ◽  
2011 ◽  
Vol 2 (2) ◽  
pp. 21-35 ◽  
Author(s):  
Tiziano Tirabassi ◽  
Alessandro Tiesi ◽  
Marco T. Vilhena ◽  
Bardo E.J. Bodmann ◽  
Daniela Buske

The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.


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