No abstract

Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE  <  5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10 ppb for Random Forest and 15 ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

The Po valley in northern Italy is renowned for its high air pollutant concentrations. Measurements of air pollutants from a background site in Modena, a town of 200 thousand inhabitants within the Po valley, are analysed. These comprise hourly data for CO, NO, NO(2), NO(x), and O(3), and daily gravimetric equivalent data for PM(10) from 1998-2010. The data are analysed in terms of long-term trends, annual, weekly and diurnal cycles, and auto-correlation and cross-correlation functions. CO, NO and NO(2) exhibit a strongly traffic-related pattern, with daily peaks at morning and evening rush hour and lower concentrations over the weekend. Ozone shows an annual cycle with a peak in July due to local production; notwithstanding the diurnal cycle dominated by titration by nitrogen oxide, the decreasing long term trend in NO concentration did not affect the long term trend in O(3), whose mean concentration remained steady over the sampling period. PM(10) shows a strong seasonality with higher concentration in winter and lower concentration in summer and spring. Both PM(10) and ozone show a marked weekly cycle in summer and winter respectively. Regressions of PM(10) upon NO(x) show a consistently greater intercept in winter, representing higher secondary PM(10) in the cooler months of the year. There is a seasonal pattern in primary PM(10) to NO(x) ratios, with lower values in winter and higher values in summer, but the reasons are unclear.

Aerosols of biogenic and anthropogenic origin affect the total radiative forcing of global climate. Poor knowledge of the pre-industrial aerosol concentration and composition, in particular of particles formed directly in the atmosphere from gaseous precursors, constitutes a large uncertainty in the anthropogenic radiative forcing. Investigations of new particle formation at preindustrial-like conditions can contribute to the reduction of this uncertainty.Here we present observations taken at the remote Nepal Climate Observatory Pyramid station at 5079 m a.s.l., a few km from the summit of Everest. We show that up-valley winds funnel gaseous aerosol precursors to higher altitudes. During this transport, these are oxidised to compounds of very low volatility, which rapidly form a large number of aerosol particles. These are then transported into the free troposphere, suggesting that the whole Himalayan region may act as an "aerosol factory", contributing substantially to the free tropospheric aerosol population. Aerosol production in this region occurs mainly via organic precursors of biogenic origin with little evidence of the involvement of anthropogenic pollutants. This process is therefore likely to be essentially unchanged since the pre-industrial period, and may have been one of the major sources contributing to the upper tropospheric aerosol population during that time.