Although microplastic pollution jeopardizes both terrestrial and aquatic ecosystems, the movement of plastic particles through terrestrial environments is still poorly understood. Agricultural soils exposed to different managements are important sites of storage and dispersal of microplastics. This study aimed to identify the abundance, distribution, and type of microplastics present in agricultural soils, water, airborne dust, and ditch sediments. Soil health was also assessed using soil macroinvertebrate abundance and diversity. Sixteen fields were evaluated, 6 of which had been exposed to more than 5 years of compost application, 5 were exposed to at least 5 years of plastic mulch use, and 5 were not exposed to any specific management (controls) within the last 5 years. We also evaluated the spread of microplastics from the farms into nearby water bodies and airborne dust. We found 11 types of microplastics in soil, among which Light Density Polyethylene (LDPE) and Light Density Polyethylene covered with pro-oxidant additives (PAC) were the most abundant. The highest concentrations of plastics were found in soils exposed to plastic mulch management (128.7 ± 320 MPs.g-1 soil and 224.84 ± 488 MPs.g-1 soil, respectively) and the particles measured from 50 to 150 μm. Nine types of microplastics were found in water, with the highest concentrations observed in systems exposed to compost. Farms applying compost had higher LDPE and PAC concentrations in ditch sediments as compared to control and mulch systems; a significant correlation between soil polypropylene (PP) microplastics with ditch sediment microplastics (r2 0.7 p 0.05) was found. LDPE, PAC, PE (Polyethylene), and PP were the most abundant microplastics in airborne dust. Soil invertebrates were scarce in the systems using plastic mulch. A cocktail of microplastics was found in all assessed matrices.

Although microplastic pollution jeopardizes both terrestrial and aquatic ecosystems, the movement of plastic particles through terrestrial environments is still poorly understood. Agricultural soils exposed to different managements are important sites of storage and dispersal of microplastics. This study aimed to identify the abundance, distribution, and type of microplastics present in agricultural soils, water, airborne dust, and ditch sediments. Soil health was also assessed using soil macroinvertebrate abundance and diversity. Sixteen fields were evaluated, 6 of which had been exposed to more than 5 years of compost application, 5 were exposed to at least 5 years of plastic mulch use, and 5 were not exposed to any specific management (controls) within the last 5 years. We also evaluated the spread of microplastics from the farms into nearby water bodies and airborne dust. We found 11 types of microplastics in soil, among which Light Density Polyethylene (LDPE) and Light Density Polyethylene covered with pro-oxidant additives (PAC) were the most abundant. The highest concentrations of plastics were found in soils exposed to plastic mulch management (128.7 ± 320 MPs.g-1 soil and 224.84 ± 488 MPs.g-1 soil, respectively) and the particles measured from 50 to 150 μm. Nine types of microplastics were found in water, with the highest concentrations observed in systems exposed to compost. Farms applying compost had higher LDPE and PAC concentrations in ditch sediments as compared to control and mulch systems; a significant correlation between soil polypropylene (PP) microplastics with ditch sediment microplastics (r2 0.7 p 0.05) was found. LDPE, PAC, PE (Polyethylene), and PP were the most abundant microplastics in airborne dust. Soil invertebrates were scarce in the systems using plastic mulch. A cocktail of microplastics was found in all assessed matrices.

Air pollution is a major global concern in urban areas and it is considered the greatest environmental risk to health. Nature-based solutions (NBS) can help improve air quality and reduce the urban heat island effect. The impacts of urban vegetation on air quality and ambient temperature depend on many factors, such as vegetation type, location, pollutants, climate conditions and topography. Therefore, the implementation of NBS needs to be tailored for each city. Within the context of the H2020 UNaLab project, the main objective of this work is to assess the potential of NBS to improve air quality across three European cities with different climates: (i) Eindhoven, The Netherlands; (ii) Tampere, Finland; and (iii) Genova, Italy. The WRF-CHEM model was applied for the hottest week in a present climate reference year. The baseline case (without NBS) and two NBS scenarios were simulated for each city. These scenarios (green roofs and green parks) were implemented in the model by modifying the land-use type and the emissions of the model grid cells. According to the model results, the city that least benefited from NBS was Tampere with an average reduction of 5% in surface temperature, and 1% in nitrogen dioxide (NO2) and ozone (O3) concentrations. Temperature-wise Genova and Eindhoven had similar results, approximately 6% reduction, while Genova showed the largest improvement in NO2 (12%). These results indicate that NBS are more effective in high temperature and high air pollution cities, such as Genova. Moreover, this study reinforces the importance of studying case-specific solutions, considering environmental characteristics and challenges.

Air pollution is a major global concern in urban areas and it is considered the greatest environmental risk to health. Nature-based solutions (NBS) can help improve air quality and reduce the urban heat island effect. The impacts of urban vegetation on air quality and ambient temperature depend on many factors, such as vegetation type, location, pollutants, climate conditions and topography. Therefore, the implementation of NBS needs to be tailored for each city. Within the context of the H2020 UNaLab project, the main objective of this work is to assess the potential of NBS to improve air quality across three European cities with different climates: (i) Eindhoven, The Netherlands; (ii) Tampere, Finland; and (iii) Genova, Italy. The WRF-CHEM model was applied for the hottest week in a present climate reference year. The baseline case (without NBS) and two NBS scenarios were simulated for each city. These scenarios (green roofs and green parks) were implemented in the model by modifying the land-use type and the emissions of the model grid cells. According to the model results, the city that least benefited from NBS was Tampere with an average reduction of 5% in surface temperature, and 1% in nitrogen dioxide (NO2) and ozone (O3) concentrations. Temperature-wise Genova and Eindhoven had similar results, approximately 6% reduction, while Genova showed the largest improvement in NO2 (12%). These results indicate that NBS are more effective in high temperature and high air pollution cities, such as Genova. Moreover, this study reinforces the importance of studying case-specific solutions, considering environmental characteristics and challenges.

In this paper, we propose improved nitrogen dioxide (NO2) to nitrogen oxide (NOx) scaling factors for several data-driven methods that are used for the estimation of NOx power plant emissions from satellite observations of NO2. The scaling factors are deduced from high-resolution simulations of power plant plumes with the MicroHH large-eddy simulation model with a simplified chemistry and then applied to Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) NO2 satellite observations over the Matimba/Medupi power stations in South Africa. We show that due to the non-linear chemistry the optimal NO2 to NOx scaling factors depend on both the method employed and the specific segments of the plume from which emission estimate is derived. The scaling factors derived from the MicroHH simulations in this study are substantially (more than 50%) higher than the typical values used in the literature with actual NO2 observations. The results highlight the challenge in appropriately accounting for the conversion from NO2 to NOx when estimating point source emissions from satellite NO2 observations.