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

Abstract

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 [NO2], 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.

The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain

“…Pollution models can help traffic managers to take decisions efficiently, by selecting the most adequate traffic management strategy [29]. In literature, meteorological data are the main input for the models [30,31], while some researchers use only traffic data [32], and a slighter proportion of researchers build their models with both traffic and meteorological data as inputs [33,34]. This manuscript will examine the relevance of road traffic variables and meteorological conditions in order to understand and predict the levels of pollutant agents in different kinds of locations of the city of Madrid, using historic traffic, pollution and meteorological data of 2015 as inputs.…”

Probabilistic Forecasting of Nitrogen Dioxide Concentrations at an Urban Road Intersection

“…The probabilities of concentrations in excess of 100 µg/m 3 are several times smaller: They range from 0.1% (value exceeded for 10 h in a year) for conditions with strong wind and low traffic flow, i.e., cluster (1,5), to 22.6% (value exceeded for 1997 h in a year) for cluster (3,1), with traffic flow in the interval (2000, 3000] and wind speed not exceeding 2 m/s. 0.05% [6] 0.01% [30] 0.01% [33] 0.00% [462] (3000,4000] 0.01% [32] 0.02% [12] 0.01% [26] The determined probability distributions indicate that for 7 of the 28 described sets of ambient conditions, the permissible value of the NO 2 concentration (200 µg/m 3 ) is reached less frequently than once per year. In unfavorable ambient conditions-cluster (3, 1)-the permissible level may be exceeded with a probability of 3.4% (300 h in a year).…”

“…A second factor determining the effectiveness of the model is the appropriate selection of explanatory variables (predictors). Most studies use sets of variables reflecting traffic and meteorological conditions [15,26,27]. There also exist studies based only on meteorological data [28] or only on traffic data [29].…”

Residuals in the modelling of pollution concentration depending on meteorological conditions and traffic flow, employing decision trees

“…The main input for the models described in the literature is traffic and meteorological data [14][15][16]. There are also some researchers who use only traffic data [17] or only meteorological data [18].…”

“…The popular and still developing multidimensional regression modelsoriginally linear, but now more complexdescribe the relationships between variables in an effective manner. González-Aparicio et al [14] used three different linear regression modelssimple linear regression, linear regression with interaction terms, and linear regression with interaction terms following Sawa's Bayesian Information Criteriato describe the dependence of PM 10 concentration on traffic, meteorological and temporal data. Betraccini et al [20] and Aldrin and Haff [21] proposed the use of a generalised additive model for modelling the shortterm effects of traffic and weather on air pollution.…”