An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology

• Published in: Environmental science and pollution research international 2013-07, Vol.20 (7), p.4469-4483
• Main Authors: González-Aparicio, I., Hidalgo, J., Baklanov, A., Padró, A., Santa-Coloma, O.
• Format: Article
• Language: English
• Subjects: Aerosols; Air Pollutants - analysis; Air pollution; Air Pollution - analysis; Air Pollution - statistics & numerical data; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Bayes Theorem; Bayesian analysis; Climate change; Earth and Environmental Science; Ecotoxicology; Emissions; Environment; Environmental Chemistry; Environmental Health; Environmental monitoring; Humidity; Linear Models; Mathematical models; Metropolitan areas; Nitrogen dioxide; Outdoor air quality; Particulate matter; Particulate Matter - analysis; Pollutants; Precipitation; Radiation; Regression analysis; Reproducibility of Results; Research Article; Seasons; Spain; Specific humidity; Statistical analysis; Studies; Time Factors; Training; Urban areas; Urban environments; Waste Water Technology; Water Management; Water Pollution Control; Wind speed
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-012-1353-7

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative—log [NO 2 ], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity—and qualitative—working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa’s Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model ( R 2  = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly ( R of 0.75–0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.