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Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

Abstract Biochar is the product of intentional pyrolysis of organic feedstocks. It is made under controlled conditions in order to achieve desired physico‐chemical characteristics. These characteristics ultimately affect biochar properties as a soil amendment. When biochar is used for carbon storage, an important property is its persistence in soil, often described by the proportion of biochar carbon remaining in soil after a 100 years (Fperm). We analyzed published data on 1230 biochars to re‐evaluate the effect of pyrolysis parameters on biochar characteristics and the possibility to predict Fperm from the maximum temperature reached during pyrolysis (HTT). We showed that biochar ash and nitrogen (N) contents were mostly affected by feedstock type. The oxygen to carbon (O:C) and hydrogen to carbon (H:C) ratios were mostly affected by the extent of pyrolysis (a combination of HTT and pyrolysis duration), except for non (ligno)cellulosic feedstocks (plastic waste, sewage sludge). The volatile matter (VM) content was affected by both feedstock type and the extent of pyrolysis. We demonstrated that HTT is the main driver of H:C ‐‐ an indicator of persistence ‐‐ but that it is not measured accurately enough to precisely predict H:C, let alone persistence. We examined the equations to estimate Fperm available in the literature and showed that Fperm calculated from HTT presented little agreement with Fperm calculated from H:C. The sign and magnitude of the bias depended on the equation used to calculate Fperm and the dispersion was usually large. This could lead to improper compensation of carbon emissions and wrong reporting of carbon sinks in national carbon accounting schemes. We recommend not to use HTT as a predictor for persistence and stress the importance to rapidly develop more accurate proxies of biochar C persistence in soil.

The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study demonstrates high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.

Resumo: Esse estudo teve como objetivo analisar produções científicas sobre o Ensino de Astronomia na Educação Básica, com enfoque nos anos iniciais do Ensino Fundamental. Especificamente, examinou um período de quinze anos (2007 a 2021) das publicações da revista Caderno Brasileiro de Ensino de Física (CBEF), da UFSC. Foram analisados trabalhos publicados antes, durante e após a aprovação da BNCC e do Ano Internacional da Astronomia, em 2009. A metodologia usada foi a revisão bibliográfica sistemática. Identificou-se consenso sobre a relevância do Ensino de Astronomia, a necessidade de formação contínua de professores e a reavaliação da formação. Observou-se um grande impacto do Ano da Astronomia na quantidade de trabalhos publicados, enquanto a implementação da BNCC relaciona-se a um aumento mais modesto. Constatou-se pouca diversidade regional e de gênero nos trabalhos, apontando para um cenário multifacetado e abrangente, em um campo de pesquisa rico a ser explorado.

The main objective of the present work was to assess the ecotoxicological safety of the use of thermochemically treated sewage sludge from the wastewater treatment plant (WWTP) of a distillery plant as a soil additive in agricultural soils based on its physicochemical characteristics and the bioaccumulation of selected elements in the plant tissues of maize (Zea mays). We have carried out physicochemical characterization (pH, EC, Corg, Cinorg, CEC, N, H, ash content, PAHs) of sewage sludge feedstock (SS) and sludge-derived biochar (BC) produced by slow pyrolysis at a temperature of 400 °C. The effect of 1% (w/w) amendment of SS and BC on soil physicochemical properties (pH, EC, Cinorg), germination of ryegrass, soil rhizobacteria and microorganisms, as well as on the accumulation and translocation of selected elements in maize (Zea mays) was studied. The results show that pyrolysis treatment of distillery WWTP sludge at 400 °C increases pH (from 7.3 to 7.7), Corg(from 28.86% to 36.83%), N (from 6.19% to 7.53%), ash content (from 23.59% to 50.99%) and decreases EC (from 2.35 mS/cm to 1.06 mS/cm), CEC (from 118.66 cmol/kg to 55.66 cmol/kg), H (from 6.76% to 1.98%) and Σ18 PAHs content (from 4.03 mg/kg to 3.38 mg/kg). RFA analysis of SS and BC showed that pyrolysis treatment multiplies chromium (Cr) (2.2 times), nickel (Ni) (2.96 times), lead (Pb) (2.13 times), zinc (Zn) (2.79 times), iron (Fe) (1.26 times) in the obtained BC, but based on an ecotoxicological test with earthworms Eisenia fetida, we conclude that pyrolysis treatment reduced the amount of available forms of heavy metals in BC compared to SS. We demonstrated by a pot experiment with a maize that a 1% addition of BC increased soil pH, decreased EC and Cinorg and had no significant effect on heavy metal accumulation in plant tissues. According to the results of the three-level germination test, it also does not affect the germination of cress seeds (Lepidium sativum). There was a significant effect of 1% BC addition on soil microbial community, and we observed a decrease in total microbial biomass and an increase in fungal species variability in the soil. Based on these results, we conclude that BC represents a promising material that can serve as a soil additive and source of nutritionally important elements after optimization of the pyrolysis process.

Ångström exponents (α) allow reconstruction of aerosol optical spectra over a broad range of wavelengths from measurements at two or more wavelengths. Hyperspectral measurements of atmospheric aerosols provide opportunities to probe measured spectra for information inaccessible from only a few wavelengths. Four sets of hyperspectral in situ aerosol optical coefficients (aerosol-phase total extinction, σ ext, and absorption, σ abs; liquid-phase soluble absorption from methanol, σ MeOH-abs, and water, σ DI-abs, extracts) were measured from biomass burning aerosols (BBAs). Hyperspectral single scattering albedo (ω), calculated from σ ext and σ abs, provide spectral resolution over a wide spectral range rare for this optical parameter. Observed spectral shifts between σ abs and σ MeOH-abs/σ DI-abs argue in favor of measuring σ abs rather than reconstructing it from liquid extracts. Logarithmically transformed spectra exhibited curvature better fit by second-order polynomials than linear α. Mapping second order fit coefficients (a 1, a 2) revealed samples from a given fire tended to cluster together, that is, aerosol spectra from a given fire were similar to each other and somewhat distinct from others. Separation in (a 1, a 2) space for spectra with the same α suggest additional information in second-order parameterization absent from the linear fit. Spectral features found in the fit residuals indicate more information in the measured spectra than captured by the fits. Above-detection σ MeOH-abs at 0.7 μm suggests assuming all absorption at long visible wavelengths is BC to partition absorption between BC and brown carbon (BrC) overestimates BC and underestimates BrC across the spectral range. Hyperspectral measurements may eventually discriminate BBA among fires in different ecosystems under variable conditions.

In this study Neem and Yellow oleander biodiesels were blended in some given proportions and characterized. An F165 diesel engine was ran with each blend and its performance parameters evaluated and compared to those obtained when the engine ran on Automotive Gas Oil (AGO). The produced neem oil biodiesel (NOB) and yellow Oleander biodiesel (YOB) were blended together in a percentage ratio of 20:80, 40:60, 60:40 and 50:50 percentages for Neem and Yellow oleander biodiesels respectively. Results show that brake power of the blends is close to that of AGO at lower torques but developed higher power at higher torques (2.69 kWh at 10Nm for N10Y40 and 2.68 kWh for AGO). The blends exhibited close comparison with the AGO in thermal efficiency. The results of the study show a diesel engine perform well with pure biodiesel blends as fuels as it does with fossil AGO, thus new biofuel was produced capable of replacing conventional diesel fuel in the transportation industry.

Printed Circuit Boards (PCBs) are essential components of electronic devices containing valuable silver metal. Using sustainable methods, silver recovery from electronic trash, like PCBs, demonstrates excellent promise. This research’s objective is to determine the optimum leaching time and solid-to-liquid (S/L) ratio for extracting silver from PCB using deep eutectic solvent (DES) composed of choline chloride and glycerol (glyceline DES). The binary DES’s leaching performance was then compared to choline chloride, glycerol, and citric acid ternary systems. Fourier Transform Infrared Spectroscopy (FTIR) was carried out to analyze the bond interactions. X-ray fluorescence spectrometry (XRF) was employed to determine the PCB’s metal concentration prior to and after the leaching process. Ternary DES was viscous, colorless, stable for 60 days, and less acidic than binary DES, with a 1.21 g/mL density. FTIR peak broadening and shifting indicated the formation of a new hydrogen bond and proved a successful synthesis of ternary DES. XRF result showed that PCB’s initial silver metal content was 2.32%. The optimal silver leaching from PCB using glyceline DES was achieved after 16 hours, with a 1/20 solid-to-liquid ratio. Ternary DES demonstrated a silver leaching efficiency of 93.65%, surpassing 86.77% of glyceline. Ternary DES synthesized in this study has the potential to serve as an efficient and environmentally friendly solvent for extracting silver from PCB, providing a sustainable approach to managing electronic waste.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the "firehose" of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.