Artificial intelligence and Eddy covariance: A review.

The Eddy Covariance (EC) method allows for monitoring carbon, water, and energy fluxes between Earth's surface and atmosphere. Due to its varying interdependent data streams and abundance of data as a whole, EC is naturally suited to Artificial Intelligence (AI) approaches. The integration of AI and EC will likely play a crucial role in the climate change mitigation and adaptation goals defined in the Sustainable Development Goals (SDGs) of the Agenda 2030.

Using biochar for environmental recovery and boosting the yield of valuable non-food crops: The case of hemp in a soil contaminated by potentially toxic elements (PTEs).

Hemp ( L.) is known to tolerate high concentrations of soil contaminants which however can limit its biomass yield. On the other hand, organic-based amendments such as biochar can immobilize soil contaminants and assist hemp growth in soils contaminated by potentially toxic elements (PTEs), allowing for environmental recovery and income generation, e.

Estimating annual GHG and particulate matter emissions from rural and forest fires based on an integrated modelling approach.

Rural and forest fires represent one of the most significant sources of emissions in the atmosphere of trace gases and aerosol particles, which significantly impact carbon budget, air quality, and human health. This paper aims to illustrate an integrated modelling approach combining spatial and non-spatial inputs to provide and enhance the estimation of GHG and particulate matter emissions from surface fires using Italy as a case study over the period 2007-2017. Three main improvements characterize the approach proposed in this work: (i) the collection and development of comprehensive and accurate data inputs related to burned area; (ii) the use of the most recent data on fuel type and load; and (iii) the modelling application to estimate fuel moisture, burning efficiency, and fuel consumption considering meteorological factors and combustion phases.

Direct observations of CO emission reductions due to COVID-19 lockdown across European urban districts.

The measures taken to contain the spread of COVID-19 in 2020 included restrictions of people's mobility and reductions in economic activities. These drastic changes in daily life, enforced through national lockdowns, led to abrupt reductions of anthropogenic CO emissions in urbanized areas all over the world. To examine the effect of social restrictions on local emissions of CO, we analysed district level CO fluxes measured by the eddy-covariance technique from 13 stations in 11 European cities.