Innovative approach to monitor GHG emissions from landfills and brownfield sites by combining ground measurements and satellite observations: first research activities for the development of a low-cost sensor toolbox

Landfills and brownfields present significant environmental risks when poorly managed, emitting pollutants into soil, water, and the atmosphere. Causes include inefficient gas collection systems and operational issues increasing emissions and fire risks, leading to environmental and health impacts. GHGs and VOCs are key indicators, highlighting management failures and environmental degradation. An effective real-time emissions monitoring from landfills and brownfield sites would improve their restoration and maintenance. Methane (CH4) is particularly critical for its powerful global warming potential, which is 28 times higher than those of CO2. However, monitoring CH4 over large surfaces is challenging, and the development of cost-effective methods remains a priority. The ESCAPE (Environmental Sites CH4 Assessment Platform Europe) project, funded through the Eureka Eurostars 3 Call, aims to develop a novel monitoring platform that integrates space-based satellite data and ground-based datasets (low-cost chemical sensors) for detecting CH4 emissions. By employing AI-based solutions, the project focuses on identifying short-lived climate pollutants, starting with CH4, to enhance the monitoring of landfills and brownfield sites. The initial phase of the project focused on testing commercial low-cost gas sensors for CH4 detection even at low concentrations (<10 ppm). Sensors including SGP40, BME688, ENS160, TGS2611, MH441D and MQ4 were selected and synchronized using a microcontroller (NUCLEO F411RE). Laboratory tests assessed their sensitivity to CH4 at different concentrations (5-10’000 ppm) also in presence of other relevant interferent gases (e.g., CO2 and other VOCs) A first prototype of the sensor toolbox has been installed at a landfill in Northern Italy. This preliminary field study enables to investigate the sensors’ behaviour in a real scenario, by testing the sensors' capability to measure CH4 emissions under real environmental conditions, with various interferences and humidity influence. Parallelly, laboratory tests are being conducted to define an advanced calibration protocol, enhancing the implementation of AI algorithms for CH4 quantification.