ArticleEnvironmental science and pollution research international2020
Evaluation of vehicular pollution levels using line source model for hot spots in Muscat, Oman.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Article
- Effect of ambient noise on indoor environments in a health care facility in Oman.Environmental science and pollution research international · 2022Article
- Assessment of noise levels and induced annoyance in nearby residential areas of an airport region in Oman.Environmental science and pollution research international · 2021Article
- Exposure assessment to road traffic noise levels and health effects in an arid urban area.Environmental science and pollution research international · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A detailed investigation was carried out to assess the concentration of near-road traffic-related air pollution (TRAP) using a dispersion model in Muscat. Two ambient air quality monitoring (AQM) stations were utilized separately at six locations near major roadways (each location for 2 months) to monitor carbon monoxide (CO) and nitrogen oxides (NOx). The study aimed to measure the concentration of near-road TRAP in a city hot spots and develop a validated dispersion model via performance measures. The US Environmental Protection Agency (US EPA) Line Source Model was implemented in which the pollutant emission factors were obtained through Comprehensive Modal Emission Model (CMEM) and COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Traffic data of all vehicle categories under normal driving conditions including average vehicle speed limits and local meteorological conditions were included in the modeling study. The analysis of monitoring data showed that hourly (00:00 to 23:00) concentrations of CO were within the US EPA limits, while NOx concentration was exceeded in most locations. Also, the measured pollutant levels were consistent with hourly peak and off-peak traffic volumes. The overall primary statistical performance measures showed that COPERT model was better than CMEM due to the high sensitivity of CMEM to the local meteorological factors. The best fractional bias (0.47 and 0.39), normalized mean square error (0.44 and 0.50), correlation coefficient (0.64 and 0.70), geometric mean bias (1.07 and 1.57), and geometric variance (2.00 and 2.32) were obtained for CO and NOx, respectively. However, the bootstrap 95% CI estimates over normalized mean square error, fractional bias, and correlation coefficient for COPERT and CMEM were found to be statistically significant from 0 in the case of combined model comparison across all the traffic locations for both CO and NOx. In overall, certain roads showed weak performance mainly due to the terrain features and the lack of reliable background concentrations, which need to be considered in the future study.
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Registered trials
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