Assessment of indoor air pollutant concentrations and emissions from natural gas cooking burners in residential buildings in Tehran, Iran

2020 ◽  
Vol 13 (4) ◽  
pp. 409-420
Author(s):  
Sama Amirkhani Ardeh ◽  
Shokooh Sadat Khaloo ◽  
Reza Gholamnia ◽  
Mehrnoosh Abtahi ◽  
Reza Saeedi
Keyword(s):  
Natural Gas ◽  
Indoor Air ◽  
Residential Buildings ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
Gas Cooking

scholarly journals Contribution of solid fuel, gas combustion, or tobacco smoke to indoor air pollutant concentrations in Irish and Scottish homes

Indoor Air ◽  
2011 ◽  
Vol 22 (3) ◽  
pp. 212-223 ◽  
Author(s):  
S. Semple ◽  
C. Garden ◽  
M. Coggins ◽  
K. S. Galea ◽  
P. Whelan ◽  
...  
Keyword(s):  
Indoor Air ◽  
Solid Fuel ◽  
Tobacco Smoke ◽  
Air Pollutant ◽  
Gas Combustion ◽  
Fuel Gas ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

scholarly journals Spatial Regression Modeling Approach for Assessing the Spatial Variation of Air Pollutants

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 785
Author(s):  
Seunghoon Park ◽  
Dongwon Ko
Keyword(s):  
Built Environment ◽  
Residential Buildings ◽  
Spatial Regression ◽  
Air Pollutant ◽  
Single Family ◽  
Socioeconomic Characteristics ◽  
Pollution Levels ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
Industrial Zones

Over the past decades, industrialization has resulted in radical economic development in Korea. The resulting urban sprawl and unsustainable development have led to considerable air pollution. In this study, using spatial regression models, we examine the effects of the physical and socioeconomic characteristics of neighborhoods on particulate matter (PM10, PM2.5), NO2, CO, and SO2 concentrations in the Daegu Metropolitan area. Results reveal the following: (i) the socioeconomic characteristics were not statistically significant regardless of the air pollutant type; (ii) the effects of the built environment characteristics of the neighborhoods were different for each air pollutant. Compared with other pollutants, PM2.5 was affected more by the built environment. Concerning the neighborhoods’ main roads, the SO2 concentration was higher, that of PM2.5 was higher in neighborhoods with more bus stops, and those of CO and PM2.5 were possibly higher in the neighborhood of industrial zones. In neighborhoods with parks and green areas, air pollutant concentrations are likely to be lower. When the total used surface of residential buildings was higher, the air pollutant concentrations were lower. Contextually, similar neighborhoods with more single-family houses seemed to have high pollution levels. Overall, this study is expected to guide policymakers and planners in making smart decisions for eco-friendly and healthy cities.

scholarly journals Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

2020 ◽  
Vol 17 (6) ◽  
pp. 2025
Author(s):  
Laura Goulier ◽  
Bastian Paas ◽  
Laura Ehrnsperger ◽  
Otto Klemm
Keyword(s):  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Air ◽  
Pollutant Concentrations ◽  
Day Of The Week ◽  
Artificial Neural ◽  
Input Variables ◽  
Air Pollutant Concentrations ◽  
Artificial Neural Network Ann ◽  
Performance Results

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

scholarly journals First steps in the development of a possible measurement method to estimate the radon concentration as an indicator of the indoor air quality

2014 ◽  
Vol 29 (suppl.) ◽  
pp. 52-58
Author(s):  
Franz Roessler ◽  
Jai Azzam ◽  
Volker Grimm ◽  
Hans Hingmann ◽  
Tina Orovwighose ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Primary Energy ◽  
Air Pollutant ◽  
Daily Routine ◽  
Air Exchange Rate ◽  
Pollutant Concentrations ◽  
Air Exchange

The energy conservation regulation provides upper limits for the annual primary energy requirements for new buildings and old building renovation. The actions required could accompany a reduction of the air exchange rate and cause a degradation of the indoor air quality. In addition to climate and building specific aspects, the air exchange rate is essentially affected by the residents. Present methods for the estimation of the indoor air quality can only be effected under test conditions, whereby the influence of the residents cannot be considered and so an estimation under daily routine cannot be ensured. In the context of this contribution first steps of a method are presented, that allows an estimation of the progression of the air exchange rate under favourable conditions by using radon as an indicator. Therefore mathematical connections are established that could be affirmed practically in an experimental set-up. So this method could provide a tool that allows the estimation of the progression of the air exchange rate and in a later step the estimation of a correlating progression of air pollutant concentrations without limitations of using the dwelling.

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