RESUMEN
Missing data has been a challenge in air quality measurement. In this study, we develop an input-adaptive proxy, which selects input variables of other air quality variables based on their correlation coefficients with the output variable. The proxy uses ordinary least squares regression model with robust optimization and limits the input variables to a maximum of three to avoid overfitting. The adaptive proxy learns from the data set and generates the best model evaluated by adjusted coefficient of determination (adjR2). In case of missing data in the input variables, the proposed adaptive proxy then uses the second-best model until all the missing data gaps are filled up. We estimated black carbon (BC) concentration by using the input-adaptive proxy in two sites in Helsinki, which respectively represent street canyon and urban background scenario, as a case study. Accumulation mode, traffic counts, nitrogen dioxide and lung deposited surface area are found as input variables in models with the top rank. In contrast to traditional proxy, which gives 20-80% of data, the input-adaptive proxy manages to give full continuous BC estimation. The newly developed adaptive proxy also gives generally accurate BC (street canyon: adjR2 = 0.86-0.94; urban background: adjR2 = 0.74-0.91) depending on different seasons and day of the week. Due to its flexibility and reliability, the adaptive proxy can be further extend to estimate other air quality parameters. It can also act as an air quality virtual sensor in support with on-site measurements in the future.
RESUMEN
To convey the severity of ambient air pollution level to the public, air quality index (AQI) is used as a communication tool to reflect the concentrations of individual pollutants on a common scale. However, due to the enhanced air pollution control in recent years, air quality has improved, and the roles of some air pollutant species included in the existing AQI as urban air pollutants have diminished. In this study, we suggest the current AQI should be revised in a way that new air pollution indicators would be considered so that it would better represent the health effects caused by local combustion processes from traffic and residential burning. Based on the air quality data of 2017-2019 in three different sites in Helsinki metropolitan area, we assumed the statistical distributions of the current indicators (NO2 and PM2.5) and the proposed particulate indicators (BC, LDSA and PNC) were related as they have similar sources in urban regions despite the varying correlations between the current and proposed indicators (NO2: r = 0.5-0.85, PM2.5: r = 0.28-0.72). By fitting the data to an optimal distribution function, together with expert opinions, we improved the current Finnish AQI and determined the AQI breakpoints for the proposed indicators where this robust statistical approach is transferrable to other cities. The addition of the three proposed indicators to the current AQI would decrease the number of good air quality hours in all three environments (largest decrease in urban traffic site, ~22 %). The deterioration of air quality class appeared more severe during peak hours in the urban traffic site due to vehicular emission and evenings in the detached housing site where domestic wood combustion often takes place. The introduction of the AQI breakpoints of the three new indicators serve as a first step of improving the current AQI before further air quality guideline levels are updated.