Modeling the short-term effect of traffic on air pollution in Torino with generalized additive models

Vehicular traffic typically plays an important role in atmospheric pollution. This is especially true in urban areas, where high pollutant concentrations are often observed. In this paper, we consider hourly measures of concentrations of nitrogen oxides (NO, NO2 and NOx), carbon oxide (CO) and particulate matter (PM), collected at the stations distributed throughout the city of Turin. To help explain the short-term behavior of the concentrations of these pollutants, we propose using generalized additive models (GAM), focusing in particular on traffic along with the meteorological predictors. All the data are collected during the period from December 2003 to April 2005.urban area, air quality, vehicular traffic, CO, NO2, NOx, NO, PM, generalized additive models

A sub-Nyquist co-prime sampling music spectral approach for natural frequency identification of white-noise excited structures

Motivated by practical needs to reduce data transmission payloads in wireless sensors for vibration-based monitoring of civil engineering structures, this paper proposes a novel approach for identifying resonant frequencies of white-noise excited structures using acceleration measurements acquired at rates significantly below the Nyquist rate. The approach adopts the deterministic co-prime sub-Nyquist sampling scheme, originally developed to facilitate telecommunication applications, to estimate the autocorrelation function of response acceleration time-histories of low-amplitude white-noise excited structures treated as realizations of a stationary stochastic process. This is achieved without posing any sparsity conditions to the signals. Next, the standard MUSIC algorithm is applied to the estimated autocorrelation function to derive a denoised super-resolution pseudo-spectrum in which natural frequencies are marked by prominent spikes. The accuracy and applicability of the proposed approach is numerically assessed using computer-generated noise-corrupted acceleration time-history data obtained by a simulation-based framework pertaining to a white-noise excited structural system with two closely-spaced modes of vibration carrying the same amount of energy, and a third isolated weakly excited vibrating mode. All three natural frequencies are accurately identified by sampling at as low as 78% below Nyquist rate for signal to noise ratio as low as 0dB (i.e., energy of additive white noise equal to the signal energy), suggesting that the proposed approach is robust and noise-immune while it can reduce data transmission requirements in acceleration wireless sensors for natural frequency identification of engineering structures