scholarly journals Mutations Induced in the Hypoxanthine Phosphoribosyl Transferase Gene by Three Urban Air Pollutants: Acetaldehyde, Benzo[a]pyrene Diolepoxide, and Ethylene Oxide

1994 ◽  
Vol 102 ◽  
pp. 135 ◽  
Author(s):  
Bo Lambert ◽  
Bjorn Andersson ◽  
Tatiana Bastlova ◽  
Sai-Mei Hou ◽  
Dennis Hellgren ◽  
...  
Keyword(s):  
Ethylene Oxide ◽  
Air Pollutants ◽  
Hypoxanthine Phosphoribosyl Transferase ◽  
Urban Air ◽  
Phosphoribosyl Transferase ◽  
Transferase Gene

Duplication in the hypoxanthine phosphoribosyl-transferase gene caused by Alu-Alu recombination in a patient with Lesch Nyhan syndrome

1993 ◽  
Vol 90 (5) ◽  
Author(s):  
Suzanne Marcus ◽  
Dennis Hellgren ◽  
Bo Lambert ◽  
SvenPetter F�llstr�m ◽  
Jan Wahlstr�m
Keyword(s):  
Hypoxanthine Phosphoribosyl Transferase ◽  
Phosphoribosyl Transferase ◽  
Transferase Gene

scholarly journals Investigation of novel mutation of the hypoxanthine phosphoribosyl-transferase gene caused by a translocation in a patient with Lesch-Nyhan syndrome.

2005 ◽  
Vol 29 (1) ◽  
pp. 21-25
Author(s):  
Makiko Mizunuma ◽  
Yasukazu Yamada ◽  
Nobuaki Wakamatsu ◽  
Nobuaki Ogasawara ◽  
Toshikazu Yamanouchi ◽  
...  
Keyword(s):  
Novel Mutation ◽  
Hypoxanthine Phosphoribosyl Transferase ◽  
Phosphoribosyl Transferase ◽  
Transferase Gene

On the Use of Satellite Data for Spatial Health Risk Assessment of Urban Air Pollutants

Epidemiology ◽  
2011 ◽  
Vol 22 ◽  
pp. S140-S141
Author(s):  
Denis Sarigiannis ◽  
Alberto Gotti ◽  
Pavlos Kalabokas ◽  
Fausto Manes ◽  
Guido Incerti ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Health Risk ◽  
Health Risk Assessment ◽  
Satellite Data ◽  
Air Pollutants ◽  
Urban Air

scholarly journals Monitoring human exposure to urban air pollutants

1993 ◽  
Vol 101 (suppl 3) ◽  
pp. 89-95 ◽  
Author(s):  
R. Barale ◽  
I. Barrai ◽  
I. Sbrana ◽  
L. Migliore ◽  
A. Marrazzini ◽  
...  
Keyword(s):  
Air Pollutants ◽  
Human Exposure ◽  
Urban Air

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.

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