Estimation of parameters in sewage sludge by near-infrared reflectance spectroscopy (NIRS) using several regression tools

Abstract

Sewage sludge application to agricultural soils is a common practice in several countries in the European Union. Nevertheless, the application dose constitutes an essential aspect that must be taken into account in order to minimize environmental impacts. In this study, near infrared reflectance spectroscopy (NIRS) was used to estimate in sewage sludge samples several parameters related to agronomic and environmental issues, such as the contents in organic matter, nitrogen and other nutrients, metals and carbon fractions, among others. In our study (using 380 biosolid samples), two regression models were fitted: the common partial least square regression (PLSR) and the penalized signal regression (PSR). Using PLSR, NIRS became a feasible tool to estimate several parameters with good goodness of fit, such as total organic matter, total organic carbon, total nitrogen, water-soluble carbon, extractable organic carbon, fulvic acid-like carbon, electrical conductivity, Mg, Fe and Cr, among other parameters, in sewage sludge samples. For parameters such as C/N ratio, humic acid-like carbon, humification index, the percentage of humic acid-like carbon, the polymerization ratio, P, K, Cu, Pb, Zn, Ni and Hg, the performance of NIRS calibrations developed with PLSR was not sufficiently good. Nevertheless, the use of PSR provided successful calibrations for all parameters.