Radon is the main source of natural radioactivity, and its measurement is considered extremely important in radioprotection, given its relationship with the occurrence of lung cancer. In the last two years, measurements of this radioactive gas were carried out in Lima considering a grid of 5 km${}^2$ and the population density to determine the number of measurements to be carried out. Cellulose nitrate nuclear track detectors exposed in bare mode and diffusion chamber mode were used to estimate ${}^{222}$Rn concentrations. In diffusion chamber mode, non-commercial monitors and commercial monitors were used. The monitoring results are presented for 43 districts of the Lima Province whose population is approximately ten million inhabitants occupying a total area of 2655.15 km${}^2$. Measurements were made obtaining an average concentration of 49 Bq·m${}^{-3}$ using bare detectors and 66 Bq·m${}^{-3}$ using non-commercial diffusion chambers. Average concentrations obtained by both detector exposure modes were below the maximum concentration recommended by the WHO. A radon (${}^{222}$Rn) map was also obtained as a visual representation of the ${}^{222}$Rn levels in the Lima province using inverse distance weighting (IDW) interpolation.

Lima City is situated on alluvial fan deposits of rivers flowing through geological formations that contain different levels of uranium. In this paper, a study is made on the average spatial and temporal behavior of radon gas in soils of Lima City. Radon concentration was determined using the LR-115 type 2 track detector during 36 periods, of 14 days each, in twenty holes distributed in the fifteen districts of Lima City. Radon concentration in soil pores ranged from 0.1 to 64.3 kBq/m3 with an average value of 5.6 kBq/m3. The average radon concentration in soil gas was about two times lower in winter than in the other seasons. High radon values during October/November 2017 were related to the earthquakes perceived in Lima City in that period. The highest radon concentrations were found in areas of alluvial deposits whose parental material has been removed from the Quilmaná and Huarangal volcanics by the Chillón and Huaycoloro Rivers. Soil gas radon concentrations were even higher in areas closer to volcanic and less distant from rivers. During the period of maximum flooding of the Chillón, Rímac and Lurín rivers, due to the natural phenomenon “El Niño Costero”, anomalous high soil radon concentrations were observed in most of the measurement sites located near rivers. These high radon values were associated with ground vibrations caused by rock and debris avalanches in rivers and creeks.

Radon exhalation is a natural process by which atoms of the radioactive gas radon diffuse in the soil and then exhale to an indoor and/or outdoor environment. High radon concentration levels, possibly from high radon exhalation rate levels, can generate an impact on public health and environmental safety, particularly in agricultural areas where prolonged exposure may affect nearby populations. While studies have examined radon exhalation, few have focused on modeling its behavior in agricultural settings or identifying key environmental and soil parameters that influence its variation. This study addresses this gap by applying Artificial Neural Network (ANN) models and Monte Carlo methods. Three distinct approaches were developed based on radon exhalation measurements from four Peruvian agricultural regions, incorporating meteorological and soil physicochemical data. First, the ANN model determined environmental factors affecting radon exhalation, achieving \(\hbox {R}^{2}\) values of 0.7949 (training) and 0.7656 (validation). Second, simulations analyzed radon diffusion under varying wind conditions, assessing dispersion risks. Third, gamma radiation measurements quantified radon progeny contributions (\(2.82 \times 10^{-4} \pm 1.15 \times 10^{-5}\) efficiency) for soil moisture detection. This integrated methodology advances understanding of agricultural radon dynamics, supporting improved radiological safety protocols and soil monitoring techniques.