Reduced anthropogenic aerosol radiative forcing caused by biogenic new particle formation

The magnitude of aerosol radiative forcing caused by anthropogenic emissions depends on the baseline state of the atmosphere under pristine preindustrial conditions. Measurements show that particle formation in atmospheric conditions can occur solely from biogenic vapors. Here, we evaluate the potential effect of this source of particles on preindustrial cloud condensation nuclei (CCN) concentrations and aerosol-cloud radiative forcing over the industrial period. Model simulations show that the pure biogenic particle formation mechanism has a much larger relative effect on CCN concentrations in the preindustrial atmosphere than in the present atmosphere because of the lower aerosol concentrations. Consequently, preindustrial cloud albedo is increased more than under present day conditions, and therefore the cooling forcing of anthropogenic aerosols is reduced. The mechanism increases CCN concentrations by 20-100% over a large fraction of the preindustrial lower atmosphere, and the magnitude of annual global mean radiative forcing caused by changes of cloud albedo since 1750 is reduced by 0.22 W m-2 (27%) to -0.60 W m-2. Model uncertainties, relatively slow formation rates, and limited available ambient measurements make it difficult to establish the significance of a mechanism that has its dominant effect under preindustrial conditions. Our simulations predict more particle formation in the Amazon than is observed. However, the first observation of pure organic nucleation has now been reported for the free troposphere. Given the potentially significant effect on anthropogenic forcing, effort should be made to better understand such naturally driven aerosol processes

Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis

Ion-induced nucleation of pure biogenic particles

Atmospheric aerosols and their effect on clouds are thought to be important for anthropogenic radiative forcing of the climate, yet remain poorly understood1. Globally, around half of cloud condensation nuclei originate from nucleation of atmospheric vapours2. It is thought that sulfuric acid is essential to initiate most particle formation in the atmosphere3,4, and that ions have a relatively minor role5. Some laboratory studies, however, have reported organic particle formation without the intentional addition of sulfuric acid, although contamination could not be excluded6,7. Here we present evidence for the formation of aerosol particles from highly oxidized biogenic vapours in the absence of sulfuric acid in a large chamber under atmospheric conditions. The highly oxygenated molecules (HOMs) are produced by ozonolysis of α-pinene. We find that ions from Galactic cosmic rays increase the nucleation rate by one to two orders of magnitude compared with neutral nucleation. Our experimental findings are supported by quantum chemical calculations of the cluster binding energies of representative HOMs. Ion-induced nucleation of pure organic particles constitutes a potentially widespread source of aerosol particles in terrestrial environments with low sulfuric acid pollution.ISSN:0028-0836ISSN:1476-468

Assessing the influence of NOx concentrations and relative humidity on secondary organic aerosol yields from alpha-pinene photo-oxidation through smog chamber experiments and modelling calculations

Secondary organic aerosol (SOA) yields from the photo-oxidation of α-pinene were investigated in smog chamber (SC) experiments at low (23–29%) and high (60–69%) relative humidity (RH), various NOx∕VOC ratios (0.04–3.8) and with different aerosol seed chemical compositions (acidic to neutralized sulfate-containing or hydrophobic organic). A combination of a scanning mobility particle sizer and an Aerodyne high-resolution time-of-flight aerosol mass spectrometer was used to determine SOA mass concentration and chemical composition. We used a Monte Carlo approach to parameterize smog chamber SOA yields as a function of the condensed phase absorptive mass, which includes the sum of OA and the corresponding bound liquid water content. High RH increased SOA yields by up to 6 times (1.5–6.4) compared to low RH. The yields at low NOx∕VOC ratios were in general higher compared to yields at high NOx∕VOC ratios. This NOx dependence follows the same trend as seen in previous studies for α-pinene SOA. A novel approach of data evaluation using volatility distributions derived from experimental data served as the basis for thermodynamic phase partitioning calculations of model mixtures in this study. These calculations predict liquid–liquid phase separation into organic-rich and electrolyte phases. At low NOx conditions, equilibrium partitioning between the gas and liquid phases can explain most of the increase in SOA yields observed at high RH, when in addition to the α-pinene photo-oxidation products described in the literature, fragmentation products are added to the model mixtures. This increase is driven by both the increase in the absorptive mass and the solution non-ideality described by the compounds' activity coefficients. In contrast, at high NOx, equilibrium partitioning alone could not explain the strong increase in the yields with RH. This suggests that other processes, e.g. reactive uptake of semi-volatile species into the liquid phase, may occur and be enhanced at higher RH, especially for compounds formed under high NOx conditions, e.g. carbonyls.ISSN:1680-7375ISSN:1680-736