The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R² values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R² values lower than 0.5 are observed.

In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.