Design, Modelling and Assessment of Emission Scenarios Resulting from a Network of Wood Biomass Boilers

Pognant, Federica; Bo, Matteo; Nguyen, Chi Vuong; Salizzoni, Pietro; Clerico, Marina

doi:10.1007/s10666-017-9563-5

Cited by 13 publications

(8 citation statements)

References 29 publications

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“…The model includes also a dedicated meteorological preprocessor, a photochemical module for NO X /O 3 reactions and dry/wet deposition units [42]. Over the last two decades, SIRANE has been validated both in wind tunnel [43] and in real case studies [42,[44][45][46][47]. Thus, this model grants a robust correlation with measured values in urban domains if its input data are carefully parametrised (especially for traffic emissions) [44,48].…”

Section: Sirane Modellingmentioning

confidence: 99%

A Combined Citizen Science—Modelling Approach for NO2 Assessment in Torino Urban Agglomeration

Salizzoni

Pognant

et al. 2020

Atmosphere

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The #CHEARIATIRA citizen science campaign was developed in February 2019 in Torino (western part of the Po Valley megacity region). The aim of the campaign was public engagement with measuring NO2 concentrations in an urban area that often exceeds air quality standards. NO2 diffusion tubes were employed by citizens under our supervision. In this paper, we present the main outcomes of a combined approach between the #CHEARIATIRA campaign and the urban dispersion model SIRANE. The results were validated against the available public Air Quality Monitoring Stations (AQMS). The citizens’ passive samplers and the modelled data show a good response in central districts both during the campaign interval and by annual projection. Traffic hotspots and sensitive receptors (schools, hospital) have high concentrations of NO2. Most of the study area (83% of the tubes) is subject to an increased risk of premature death according to epidemiological literature.

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Section: Sirane Modellingmentioning

confidence: 99%

A Combined Citizen Science—Modelling Approach for NO2 Assessment in Torino Urban Agglomeration

Salizzoni

Pognant

et al. 2020

Atmosphere

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“…Climate change is currently generating more and more intense and frequent extreme rainfall. The causes that generate climate change are under investigation and are mainly attributable to pollutant emissions [1], so much so that many studies evaluate their reduction [2]. In particular the greenhouse gases have increased from 1970 to 2010 of 0.55 gigatons per year, with a growth in the last period 2000-2010 of 1 gigaton per year despite the increasing countermeasures adopted by nations [3].…”

Section: Aim Of the Study And State Of The Artmentioning

confidence: 99%

Analysis of Rainfall Trends and Extreme Precipitation in the Middle Adriatic Side, Marche Region (Central Italy)

Gentilucci

Barbieri

Lee

et al. 2019

Water

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Extreme precipitation trends and events are fundamental for the definition of the region’s climate and allow the subsequent analysis of the risk for the territory and the possible countermeasures. This study takes into account the Marche Region (Central Italy) with 128 rain gauges from 1921 to 2017. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. In addition, to obtain a comparable analysis, the average precipitations grouped in climatological standard normals were analyzed. Finally, the main purpose of the research was achieved by analyzing extreme events in the middle Adriatic side. In addition, forecasts of extreme precipitation events, with a return period of 100 years, were made using the theory of “generalized extreme value” (GEV). The innovation of this research is represented by the use of geostatistics to spatialize the variables investigated, through a clear and immediate graphic representation performed through GIS software. This study is a necessary starting point for the study of climate dynamics in the region, and it is also a useful tool for land use planning.

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“…In a heating system that used a mixture of 20% straw and 80% coal, Chowdhury et al ( 2019 ) found that increasing the proportion of straw in the mixture reduced nitrous oxide and sulfur dioxide emissions, demonstrating the carbon reduction effect of biomass. However, Pognant et al ( 2018 ) showed that woodchip boilers might not be as environmentally friendly as natural gas boilers locally but can still significantly improve local air quality by reducing ground-level particulate matter concentrations. Thus, biomass boiler heating can be an effective means of reducing greenhouse gas emissions while having the potential to cause pollution from other gases.…”

Section: Green Solutions For Home Heatingmentioning

confidence: 99%

Strategies to save energy in the context of the energy crisis: a review

et al. 2023

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New technologies, systems, societal organization and policies for energy saving are urgently needed in the context of accelerated climate change, the Ukraine conflict and the past coronavirus disease 2019 pandemic. For instance, concerns about market and policy responses that could lead to new lock-ins, such as investing in liquefied natural gas infrastructure and using all available fossil fuels to compensate for Russian gas supply cuts, may hinder decarbonization efforts. Here we review energy-saving solutions with a focus on the actual energy crisis, green alternatives to fossil fuel heating, energy saving in buildings and transportation, artificial intelligence for sustainable energy, and implications for the environment and society. Green alternatives include biomass boilers and stoves, hybrid heat pumps, geothermal heating, solar thermal systems, solar photovoltaics systems into electric boilers, compressed natural gas and hydrogen. We also detail case studies in Germany which is planning a 100% renewable energy switch by 2050 and developing the storage of compressed air in China, with emphasis on technical and economic aspects. The global energy consumption in 2020 was 30.01% for the industry, 26.18% for transport, and 22.08% for residential sectors. 10–40% of energy consumption can be reduced using renewable energy sources, passive design strategies, smart grid analytics, energy-efficient building systems, and intelligent energy monitoring. Electric vehicles offer the highest cost-per-kilometer reduction of 75% and the lowest energy loss of 33%, yet battery-related issues, cost, and weight are challenging. 5–30% of energy can be saved using automated and networked vehicles. Artificial intelligence shows a huge potential in energy saving by improving weather forecasting and machine maintenance and enabling connectivity across homes, workplaces, and transportation. For instance, 18.97–42.60% of energy consumption can be reduced in buildings through deep neural networking. In the electricity sector, artificial intelligence can automate power generation, distribution, and transmission operations, balance the grid without human intervention, enable lightning-speed trading and arbitrage decisions at scale, and eliminate the need for manual adjustments by end-users.

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Design, Modelling and Assessment of Emission Scenarios Resulting from a Network of Wood Biomass Boilers

Cited by 13 publications

References 29 publications

A Combined Citizen Science—Modelling Approach for NO2 Assessment in Torino Urban Agglomeration

A Combined Citizen Science—Modelling Approach for NO2 Assessment in Torino Urban Agglomeration

Analysis of Rainfall Trends and Extreme Precipitation in the Middle Adriatic Side, Marche Region (Central Italy)

Strategies to save energy in the context of the energy crisis: a review

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