Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy

Fassò, Alessandro; Rodeschini, Jacopo; Moro, Alessandro Fusta; Qendrim, Shaboviq,; Maranzano, Paolo; Cameletti, Michela; Finazzi, Francesco; Golini, Natalia; Ignaccolo, Rosaria; Otto, Philipp

doi:10.1038/s41597-023-02034-0

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…Anyway, it can be assumed that the values obtained do not differ from the levels generally observed in urban areas, both residential and industrial sites. Numerous studies have reported exposure to air pollutants and often refer to single cities, short time periods, and speci c air pollutants (Fassò et al 2023;Gilardi et al 2023). In particular, the phenomena of air pollution from benzene-based molecules (benzene is a ubiquitous pollutant of indoor and outdoor air, as well as class 1 carcinogenic substance, according to IARC -International Agency for Research on Cancer) have been extensively described in the recent literature, referring to various regions of Italy (Martellini et Ciacci et al 2023).…”

Section: Voc Pro Lesmentioning

confidence: 99%

Determination of Volatile Organic Compounds (VOCs) in indoor work environments by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry

Marchesiello,

Spadaccino,

Usman

et al. 2024

Preprint

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Volatile Organic Compounds (VOCs) are continuously emitted into the atmosphere from natural and anthropogenic sources and rapidly spread from the atmosphere to different environments. A large group of VOCs has been included in the class of air pollutants, therefore, their determination and monitoring using reliable and sensitive analytical methods represents a key aspect of health risk assessment. In this work, an untargeted approach is proposed for the evaluation of the exposure to volatile organic compounds of workers in an engine manufacturing plant by GC-MS measurements, coupled with Solid-Phase Microextraction (SPME). The analytical procedure was optimized in terms of SPME fiber, adsorption time, desorption time, and temperature gradient of the chromatographic run. For the microextraction of VOCs, the SPME fibers were exposed to the air in two different zones of the manufacturing factory, i.e., in the mixing painting chamber and the engine painting area. Moreover, the sampling was carried out with the painting system active and running (system on) and with the painting system switched off (system off). Overall, 212 compounds were identified, but only 17 were always present in both zones (mixing painting chamber and engine painting area), regardless of system conditions (on or off). Finally, a semi-quantitative evaluation was performed considering the peak area value of the potentially most toxic compounds by multivariate data analyses.

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Section: Voc Pro Lesmentioning

confidence: 99%

Determination of Volatile Organic Compounds (VOCs) in indoor work environments by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry

Marchesiello,

Spadaccino,

Usman

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The overall dataset examined in the present study was characterized, with respect to previous experiences of data collection for Agrimonia [43], for the use of the bottom-up LPi, together with the hourly data on ammonia concentrations and the main meteorological parameters, in long-term time series.…”

Section: Annual Emission Estimatesmentioning

confidence: 99%

Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations

Marongiu,

Collalto,

Distefano

et al. 2024

Air

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This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and contributes to secondary PM2.5. The methodology was implemented to understand the effects of weather and emission changes on atmospheric ammonia concentrations. The model was trained and tested by hourly measurements of ammonia concentrations and atmospheric turbulence parameters, starting from a constant emission scenario. The initial values of emissions were calculated based on a bottom-up emission inventory detailed at the municipal level and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations for each iteration, the emissions were modified, the model’s training and testing were repeated, and the model converged to a very high performance in predicting ammonia concentrations and establishing hourly time-varying emission profiles. The ammonia concentration predictions were extremely accurate and reliable compared to the measured values. The relationship between NH3 concentrations and the calculated emissions rates is compatible with physical atmospheric turbulence parameters. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow the identification of the effect of atmospheric turbulence on pollutant accumulation. The proposed methodology is suitable for validating and confirming emission time series and defining highly accurate emission profiles for the improvement of the performances of chemical and transport models (CTMs) in combination with in situ measurements and/or optical depth from satellite observation.

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“…The overall dataset examined in the present study is characterized, with respect to previous experiences of data collection Agrimonia [37], for the use of the bottom up LPi, together with the hourly data of ammonia concentrations and the main meteorological parameters on long-term time series.…”

Section: Meteorological Parameters Measurements Sitesmentioning

confidence: 99%

Application of Machine Learning to Estimate Ammonia Atmospheric Emissions

Marongiu,

Collalto,

Distefano

et al. 2023

Preprint

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Ammonia is an atmospheric pollutant, predominantly emitted from agriculture, leading acidification and eutrophication of soil and water and contributing to secondary PM2.5. The implementation of accurate emission inventories with high spatial and time resolution plays a fundamental role in the development of air modelling simulation and in the impact assessment of actions for air quality improvement. The development and release of new algorithms and the increase of data availability are supporting the implementation of machine learning approaches in environmental and air quality data analysis. In this paper we present a methodology developed by the application of the Random Forest algorithm to bottom-up local emission inventories of ammonia to validate annual time series of ammonia emissions and calculate high resolution temporal profiles. The model has been trained and tested by the hourly measurements of ammonia concentrations and atmospheric turbulence parameters starting from a constant emission scenario. The initial values of emissions are calculated based on a bottom-up emission inventory detailed at the municipal basis and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations, the emissions are modified, the model's training and testing are repeated, and the model converges to a very high performance in predicting ammonia concentrations and establishing an hourly time changing emission profile. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow to identify the effect of atmospheric turbulence on pollutant accumulation. The estimated time series well confirm the available data of the emission inventories and the monthly emission profiles have been compared with estimated data from satellite.

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Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy

Cited by 14 publications

References 28 publications

Determination of Volatile Organic Compounds (VOCs) in indoor work environments by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry

Determination of Volatile Organic Compounds (VOCs) in indoor work environments by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry

Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations

Application of Machine Learning to Estimate Ammonia Atmospheric Emissions

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