In The Science of the total environment
Photocatalytic technology implemented in construction materials is a promising solution to contribute to alleviate air quality issues found in big cities. Photocatalysis has been proved able to mineralise most harmful contaminants. However, important problems associated with monitoring the efficiency of these solutions under real conditions still remain, including the lack of affordable analytical tools to measure NOx concentrations with enough accuracy. In this work, two pilot scale demonstration platforms were built at two different locations to assess the photocatalytic NOX removal efficiency of ten selected materials exposed outdoors for AQmesh low-cost sensor PODs were used to measure ground-level to measure NO and NO2 concentrations during nearly one year. The pollutant removal efficiency of the materials was then calculated based on a comparison with simultaneously concentration measurements carried-out on reference, non-active materials. It was found that the NO2 removal efficiency presented large variations across the seasons, with maxima during the warmer months, while NO efficiencies were comparatively steadier. Statistical analysis delivered evidence that the efficiencies significantly depend on different meteorological variables (irradiance and relative humidity) besides NO, NO2 ambient concentrations. Lower efficiencies were observed for higher concentration levels and vice versa. The influence of water vapour could be related to two different effects: a short-term contribution by the instantaneous air humidity and a long-term component associated with the hygroscopic state of the material. The contribution of wind to the pollutant removal efficiencies was principally related to the humidity of air masses moving above the location and to the advection of pollutants from specific emission sources.
Cordero J M, Hingorani R, Jimenez-Relinque E, Grande M, Borge R, Narros A, Castellote M
Air quality, Construction materials, Environmental variables, Heterogeneous photocatalysis, Machine learning, NO(x) removal efficiency