Carbon Chemist

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  • Causes, impacts, and future projections of Arctic sea ice loss

    Causes, impacts, and future projections of Arctic sea ice loss

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    Arctic sea ice loss is expected to continue over the next decades, with implications for the climate system and Arctic communities and ecosystems.

    Arctic sea ice: A defining feature of the Arctic

    Arctic sea ice forms from freezing ocean water during the dark, cold Arctic winters. In the 1980s, the sea ice stretched across the entire Arctic Ocean in winter, even reaching parts of the adjacent North Pacific and North Atlantic Oceans.

    Each spring, as sunlight returns, the ice melts, but even at its lowest point in September, it still covers half of the Arctic Ocean. The Arctic Ocean’s year-round sea ice cover is a key feature of the Arctic as a region dominated by ice and snow.

    The rapid decline of Arctic sea ice in recent decades

    Over the past four decades, Arctic sea ice has declined significantly in both area and thickness. Since the start of continuous satellite observations in late 1978, we have seen a 50% reduction in the sea ice area in September (Fig. 1).

    Therefore, during the seasonal minimum in September, the sea ice now covers only about 25% of the Arctic Ocean (Fig. 2). At the same time, the ice has also become thinner, as shown by measurements from submarines and, more recently, satellites.

    Fig. 1: Observed sea ice area in September from the NSIDC Climate Data Record

    Climate models show that the dramatic loss of Arctic sea ice over the past 40 years can only be explained by the influence of human-caused greenhouse gas emissions. When models are run with only natural factors, such as changes in solar radiation or volcanic activity, they fail to replicate the observed 50% reduction in Arctic sea ice area in September.

    Impacts of Arctic sea ice loss

    The sharp decline in Arctic sea ice cover in September is one of the most visible signs of human-driven greenhouse gas emissions (Fig. 2). However, the importance of sea ice loss goes far beyond symbolism. For example, the changing sea ice cover already affects Indigenous communities in the Arctic that rely on sea ice for hunting and transportation.

    Furthermore, when sea ice melts, the bright, reflective surface of the Arctic is replaced by the darker ocean beneath. Unlike ice, which reflects much of the sun’s energy like a mirror, the darker ocean absorbs much more sunlight, warming the surface waters. This added heat accelerates sea ice melt, exposing even more dark oceans and further increasing heat absorption. This feedback loop is one of the key reasons why the Arctic has warmed two to four times faster than the global average and why sea ice loss has been so large in recent decades.

    Fig.2: Sea Ice Concentration from the NSIDC Climate Data Record

    What happens in the Arctic doesn’t stay in the Arctic, as the region is closely linked to lower latitudes through ocean currents and atmospheric circulation. As a result, the increased energy absorbed by the Arctic Ocean from melting sea ice ultimately influences the climate in regions further south as well.

    Projections of future Arctic sea ice loss and its impacts

    Climate models agree that under continued global warming, the Arctic sea ice cover will continue to decrease in the coming decades.

    This reduction is expected to persist through at least the mid- to late 2040s and across all months of the year, regardless of the greenhouse gas emission scenario considered. It is only in the late 2040s that differences in different possible future emissions pathways begin to significantly influence the extent of the Arctic sea ice cover.

    As Arctic sea ice continues to decline, the Arctic Ocean absorbs increasing amounts of solar energy, triggering profound changes. One notable impact is a shift in ecosystems: as waters warm, fish species from the North Atlantic are likely to expand into newly hospitable areas of the Arctic Ocean. At the same time, the loss of sea ice threatens the survival of species such as polar bears and ringed seals, which depend on the ice for breeding and hunting. These ecological changes, coupled with the extended absence of sea ice near shorelines, will further disrupt the traditional hunting and transportation practices of Arctic Indigenous communities.

    Less sea ice in the Arctic, especially in summer, also means it is more accessible to commercial shipping and tourism. In recent years, the number of ships crossing the Arctic has risen and is expected to grow as sea ice continues to decline. The reason for that is that the Arctic shipping route offers a faster and more cost-effective path between Europe or the US West Coast and Asia.

    However, this increased traffic through the Arctic Ocean requires planning for search and rescue operations and disaster response in the remote Arctic Ocean. In addition, a more navigable Arctic Ocean has geopolitical implications.

    Notably, all these impacts increase the more sea ice the Arctic loses.

    An ice-free Arctic Ocean by 2050

    Once the remaining sea ice area is equal to or less than 1 million km2, scientists consider the Arctic to be `practically ice-free.’ At that point, the remaining sea ice will be limited to the area north of Greenland and the Canadian Archipelago, leaving over 93% of the Arctic Ocean without sea ice.

    The Arctic Ocean has not experienced ice-free conditions for over 80,000 years, meaning that modern humans have never encountered an ice-free Arctic.

    Current climate models, which informed the 2021 Intergovernmental Panel on Climate Change Assessment Report, project a 66% probability that the Arctic will experience its first ice-free September by 2050. However, ice-free conditions could also occur decades earlier or later than 2050, as climate model projections are always probabilistic.

    The probabilistic nature of climate projections arises from the chaotic behaviour of the atmosphere and ocean, which limits precise predictions, much like the inability to forecast weather beyond about ten days.

    Climate models show that staying below global warming of 1.5°C is the only way to avoid ice-free conditions in the Arctic.

    However, even at global warming of 1.5°C, occasional ice-free months may still occur. Thus, while completely preventing ice-free conditions may no longer be feasible, the models also show that reducing future greenhouse gas emissions can limit the extent of sea ice loss.

    For instance, if warming exceeds 2.5°C, the ice-free season could extend for four months of the year, from July to October. Hence, any reduction in global greenhouse gas emissions decreases the area and duration of open water present in the Arctic Ocean, mitigating some of the impacts.

    What will Arctic sea ice cover look like at the end of the 21st century?

    The largest source of uncertainty in climate projections for the end of the 21st century is the amount of greenhouse gas emissions between now and then. Climate models simulate a range of possible outcomes for the Arctic sea ice cover based on different emission scenarios, which predict global warming levels between about 1.5°C and over 5°C by 2100.

    Under lower warming scenarios (around 1.5°C), the Arctic sea ice cover in summer could still resemble its current state. However, with warming exceeding 3°C, the Arctic could become ice-free for several months each year (Fig. 3).

    Fig. 3: Projected Sea Ice Concentration from CMIP6 models by 2100 for different global warming levels

    In summary, the future state of Arctic sea ice cover will depend entirely on global greenhouse gas emissions. Any reductions in emissions will mean that more sea ice will persist in the Arctic Ocean.

    This article is based on research funded by the US National Science Foundation.

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  • New particle formation from isoprene under upper-tropospheric conditions

    New particle formation from isoprene under upper-tropospheric conditions

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    The CLOUD experiments

    The CERN CLOUD chamber36 was used to conduct the experiments presented in this study. CLOUD is an electropolished, stainless-steel, 26.1-m3 chamber designed to study new particle formation under the full range of tropospheric and lower-stratospheric conditions. The thermal housing around the chamber is able to control the temperature from 208 to 373 K with high precision (±0.1 K)51. CLOUD was operated at a pressure of approximately 965 ± 5 mbar in this study. To avoid cross-contamination between different experimental programmes and to achieve extremely low NH3 concentrations, the chamber is cleaned by rinsing the chamber walls with ultrapure water and heating to 373 K for more than 24 h. To maintain cleanliness and ensure minimal contamination, ultrapure synthetic air—derived from mixing cryogenic liquids (21% oxygen and 79% nitrogen)—is continuously injected into the chamber. The chamber is characterized by a low loss rate, with condensation sink values comparable with those observed in pristine environments.

    Various light sources are positioned in the CLOUD chamber to selectively drive photochemistry. OH production is initiated by illuminating O3 with a UV fibre-optic system, a combination of four 200-W Hamamatsu Hg-Xe lamps with wavelengths spanning 250 and 450 nm, a krypton fluoride (KrF) excimer UV laser at 248 nm and a 52-W low-pressure mercury lamp centred at 254 nm. As well as O3 photolysis, OH radicals are also produced by photochemical production from nitrous acid (HONO) and hydrogen peroxide (H2O2). Both the HONO and H2O2 generators were designed specifically for CLOUD experiments. Following the same principle as an earlier study52, a gas–liquid mixture of HONO is synthesized from continuous mixing of H2SO4 (Sigma Aldrich, 99%) with sodium nitrite (NaNO2, Sigma Aldrich, 99%) in a stainless-steel reactor53. HONO is transferred from liquid phase to gas phase by flowing nitrogen gas (1–2 l min−1) through the reactor. HONO is then introduced into the CLOUD chamber and photolysed by a UV light source centred at 385 nm to produce OH radicals and NO. The HONO reactor is continuously cooled to 5 °C and a cryo-trap is placed between the reactor and the chamber to remove excess water vapour and avoid ice blockage of the chamber input pipe. Gaseous H2O2 is produced from bubbling N2 gas through a H2O2 solution. The H2O2 solution is stored in a glass beaker contained in a stainless-steel container at a constant temperature of 5 °C. A different combination of UV sources is used to photolyse H2O2 to produce different amounts of OH radicals.

    A green light sabre centred at 528 nm is used to photolyse molecular iodine (I2). All light systems are continuously monitored by a spectrometer and an array of photodiodes at the bottom of the chamber. Dedicated actinometry experiments allow quantitative determination of actinic fluxes of the light system at different intensities.

    Particle formation under different ionization regimes is simulated by combining a strong electric field (±30 kV) and the pion beam produced by the CERN Proton Synchrotron. The electric field eliminates natural ions in less than 1 s, thus creating ion-free conditions (neutral experiments). The pion beam produced by the CERN Proton Synchrotron enhances ion production on top of the galactic cosmic rays. Two magnetically coupled stainless-steel fans mounted at the top and bottom of the chamber enable uniform spatial mixing of particles and vapours within a few minutes. Iodine is injected into the chamber from a temperature-controlled evaporator containing crystalline iodine (I2, Sigma-Aldrich, 99.999% purity) at the bottom of the chamber. The SO2 (Carbagas, 100 parts per million by volume (ppmv) in N2) and isoprene (PanGas, 1,000 ppmv in N2) are injected into the chamber from pressurized gas cylinders and the O3 is introduced to the chamber by passing O2 through an ozone generator.

    The data presented in this study were collected in two consecutive CLOUD campaigns (CLOUD15 and CLOUD16). The CLOUD15 and CLOUD16 campaigns were carried out from September to November in 2022 and 2023, respectively. Because the experiments reported in this study were carried out at extremely low temperatures (−30 °C and −50 °C), heat-insulation systems (CLOUD15) and active cooling systems (CLOUD16) were used to reduce measurement systematic error. The heat-insulation systems were primarily made with thermal insulation foam to isolate the instrument inlet system from ambient air. The active cooling systems involved circulating the air inside the chamber thermal housing, at the same temperature as the chamber, around the inlet systems of different instruments. The active cooling systems were also wrapped with thermal insulation foam to allow for more effective inlet cooling. These cooling systems were applied to all mass spectrometers and particle counters, except a butanol condensation particle chamber (CPC; TSI 3776), a nano-scanning mobility particle sizer (nano-SMPS, TSI 3938) and a long-SMPS (TSI 3082), which used a heat-insulation system in both campaigns to act as a standard to avoid systematic errors resulting from changing from the heat-insulation system to the active cooling system.

    Measurement of chemical composition

    Ozone (O3)

    O3 was monitored using a gas monitor (Thermo Environmental Instruments, TEI 49C).

    Hydroxyl radicals (OH)

    The OH radical was measured by HORUS54 (HydrOxyl Radical measurement Unit based on fluorescence Spectroscopy).

    Hydroperoxyl radical (HO2)

    The HO2 radical was primarily measured using the bromide chemical-ionization mass spectrometer coupled with a multi-scheme chemical-ionization inlet-2 (Br-MION2-CIMS)55 and HORUS in both CLOUD15 and CLOUD16 campaigns. HORUS measures HO2 by chemically converting it to OH by NO. However, the RO2 radical (organic peroxy radicals) produced from isoprene oxidation may also contribute to the HO2 signal measured by HORUS, as the reaction between RO2 + NO can also produce OH radicals. By contrast, the HO2 measurement by Br-MION2-CIMS is less ambiguous, as it is defined by the peak HO2Br− (ref. 55). However, the measurement of HO2 by Br-MION2-CIMS is severely affected by air–water content55, making offline calibration difficult. Therefore, the HO2 measurement by Br-MION2-CIMS was calibrated by HORUS under RO2 radical-free conditions. The online calibration was carried out for every absolute humidity condition reported in this manuscript. During a small section in which the primary ions of Br-MION2-CIMS were saturated by either HONO or H2O2, either the low-pressure bromide chemical-ionization mass spectrometer or HORUS was used to complement the HO2 measurement after intercomparing the data with Br-MION2-CIMS and HORUS during experiments without HONO and H2O2. The precision of OH and HO2 data acquired by the HORUS instrument is quantified at 13% and 7%, respectively, with uncertainties calculated at 1σ over a 10-min averaging period. Furthermore, the systematic error of the measurement is calculated to be 12% for OH and 30% for HO2.

    Nitrogen oxide (NO) and dioxide (NO2)

    NO was measured by detecting the chemiluminescence of NO and O3 using a chemiluminescence detector (ECO PHYSICS, CLD 780TR). This instrument was calibrated by a second NO monitor (ECO PHYSICS, CLD 780TR), which—in turn—was calibrated using the CMK5 Touch dilution system (Umwelttechnik MCZ GmbH) with a NO bottle (Praxair, 1.00 ppmv in N2) and synthetic air (Nippon Gases, hydrocarbon-free). The first detector, which provides data for this study, was found to contain background values that have been subtracted in this study. NO2 was measured by a cavity-attenuated phase-shift nitrogen dioxide monitor (CAPS NO2, Aerodyne Research Inc.). Hourly, the instrument undergoes a 5-min background measurement of pure N2 gas. During the 5-min background measurements, data have been interpolated to give a continuous time series. The NO2 monitor was calibrated using a custom-made cavity-enhanced differential optical absorption spectroscopy instrument56. After the subtraction of an average instrument background concentration, the final NO2 concentration was obtained.

    Nitrous acid (HONO) and hydrogen peroxide (H2O2)

    Both HONO and H2O2 were detected using bromide chemical-ionization mass spectrometry55, as they exhibit reasonable affinity with the bromide anion. Direct calibrations of these two species were not carried out on-site and the current estimation assumes that they share the same detection sensitivity as H2SO4 (a low-limit estimation). Because these species serve as the precursors of OH and NO radicals, which were reliably traced, the concentrations of HONO and H2O2 are not crucial to the reported results and are therefore omitted from this study.

    Two bromide chemical-ionization systems were used to detect HONO and H2O2. The first system, Br-MION2-CIMS, offers sensitive detection of both species at concentrations below about 1010 cm−3, with a detection limit of around 6 × 106 cm−3 (H2O2) and 1.6 × 105 cm−3 (HONO). However, in some experiments, the estimated HONO and H2O2 concentrations exceeded 1010 cm−3. The second system, Br-AIM-CIMS, uses bromide chemical-ionization at low pressure in combination with an active water feedback loop to control the Br-hydration in the ion molecule reactor and avoids saturation. Br-AIM-CIMS was used to measure concentrations from above the detection limit of 4.8 × 107 cm−3 (HONO) and 3.3 × 107 cm−3 (H2O2), based on a calibration factor of 3 × 1012 for HONO and H2O2.

    Sulfur dioxide (SO2)

    To measure the concentration of SO2, a gas monitor (Thermo Fisher Scientific Model 42i-TLE) was used. However, as the SO2 concentrations in our experiments were usually below 5 × 109 cm−3 (150 pptv), we also used the Br-MION2-CIMS to measure SO2 (ref. 55) in both CLOUD15 and CLOUD16 campaigns. The measurement of SO2 by Br-MION2-CIMS is substantially affected by air–water content, so we conducted online SO2 calibration using the SO2 monitor at both −30 °C and −50 °C. The derived calibration factors are 1.7 × 1013 at −30 °C and 1.5 × 1011 at −50 °C for CLOUD15 and 3.1 × 1011 at −50 °C for CLOUD16. During the experiments, when the primary ions of Br-MION2-CIMS were saturated by either HONO or H2O2, the Br-AIM-CIMS was used to complement the SO2 measurement. With an active water sensitivity control, Br-AIM-CIMS measures SO2 concentrations from above the detection limit of 3 × 107 cm−3 with a constant calibration factor of 20 × 1012 at −30 °C and −50 °C.

    Sulfuric acid (H2SO4)

    To ensure the quality of the reported data, we monitored H2SO4 concentrations using two chemical-ionization mass spectrometers: the nitrate chemical-ionization mass spectrometer (NO3-CIMS) and the MION2-CIMS operating in bromide chemical-ionization mode (Br-MION2-CIMS55). Furthermore, isotopically labelled H15NO3 was used during the CLOUD16 campaign to distinguish the nitrogen atom originating from the analyte with the reagent ion. The H2SO4 calibration was carried out by two independent calibration systems. The first set-up used the original calibration box designed by Kürten et al.57 along with their in-house calibration scripts. The second set-up is similar to the original version but with different physical dimensions. Also, the recently developed open-source MARFORCE model is used to simulate H2SO4 production in both calibration set-ups55.

    In total, we conducted seven calibration experiments at different stages of the CLOUD15 campaign, and each CIMS instrument was calibrated using both calibration set-ups. Two calibrations were performed for the Br-MION2-CIMS, resulting in equivalent H2SO4 calibration factors of 157% and 149%. For the NO3-CIMS, five calibrations were carried out, resulting in equivalent calibration factors of 88%, 100%, 95%, 154% and 164%. Given that the NO3-CIMS provided most of the H2SO4 concentration in this study, we use the calibration carried out immediately after the experiments for this study. This results in a calibration factor of 6.2 × 109 cm−3 for the NO3-CIMS and an equivalent calibration factor of 9.0 × 109 cm−3 for the Br-MION2-CIMS. We use the minimum and maximum of the seven calibrations, ranging from 88% to 164%, as the systematic error of the H2SO4 detection for CLOUD15. It is important to note that we had to change the optimal inlet flow rates of the Br-MION2-CIMS at −30 °C and −50 °C. The varying temperatures and flow rates result in different inlet loss rates, all of which have been accounted for in this dataset.

    As well as the normal H2SO4 calibration, we conducted a set of iodine oxoacid nucleation experiments at −10 °C, similar to those presented in ref. 37. The nucleation rates in these experiments are comparable with all of our earlier experiments, further enhancing our confidence in the reported acid concentrations.

    In the CLOUD16 campaign, a total of seven calibration experiments were carried out. Two calibration experiments were conducted for the Br-MION2-CIMS, before and after the presented experiments. The results yield equivalent H2SO4 calibration factors of 120% and 118%. For the labelled NO3-CIMS, six calibrations were performed in total, three before the isoprene experiments, resulting in equivalent calibration factors of 100%, 99% and 88%. It is important to note that, during the last few days of the isoprene experiments, the NO3-CIMS suffered from a pump failure that may have caused a shift (by up to 20%) in the calibration factor owing to a slight change in the sample flow. This potentially affects only two experiments in this study. To correct for this, we have assumed a linear correlation between the sample flow and calibration factor. The failing pumps were then replaced and the data from the rest of the experiments were calibrated after the presented experiments, with two calibrations that yielded equivalent calibration factors of 190% and 185%. This yields a calibration factor of 1 × 1010 cm−3 for the labelled NO3-CIMS and an equivalent calibration factor of 1.9 × 1010 cm−3 for the Br-MION2-CIMS. By considering all of the calibration experiments, the systematic error of H2SO4 detection for CLOUD16 is estimated to range from 88% to 120%. Furthermore, using these two instruments, after applying their respective calibration factors, we compared the measured methanesulfonic acid concentrations from the CLOUD chamber at −50 °C. This comparison demonstrated a good agreement, confirming the accuracy of the calibrations.

    Iodine species

    We measured iodic acid (HIO3) and iodous acid (HIO2) using both the NO3-CIMS and Br-MION2-CIMS and we use the same calibration factor as H2SO4 in the data analysis, similar to our earlier studies37,45,55,58,59. We used Br-MION2-CIMS to measure I2, which is detected at the collision limit, shown by our recent studies55,60.

    Isoprene

    Isoprene was measured by a proton transfer reaction mass spectrometer using the hydronium chemical-ionization method61 (H3O-PTR-MS). This particular instrument used in this study is an adapted version, which is explained in greater detail previously62.

    ISOPOOH and IEPOX detection and separation

    Measuring and distinguishing between ISOPOOH and IEPOX can be experimentally challenging owing to their identical molecular formula (C5H10O3). As a result, mass-spectrometric methods often detect them together at the same exact mass in the same peak35. To address this issue, techniques such as tandem mass spectrometry have been used to separate ISOPOOH and IEPOX from each other29.

    In this study, these two isomeric compounds were measured both by the Br-MION2-CIMS and the proton transfer reaction mass spectrometer 3 (ref. 63) operating in ammonium chemical-ionization mode (NH4-PTR3-CIMS64). NH4-PTR3-CIMS measured ISOPOOH and IEPOX primarily as clusters with ammonium cation, as the proton affinity (see the ‘Quantum-chemical calculations’ section) of NH3 (204.25 kcal mol−1) is higher than that of 1,2-ISOPOOH (198.31 kcal mol−1), 4,3-ISOPOOH (195.51 kcal mol−1) and cis-β-IEPOX (204.11 kcal mol−1). In this study, we also aim to investigate the capability of the Br-MION2-CIMS in detecting ISOPOOH and IEPOX. We calculate the formation free enthalpies of 1,2-ISOPOOH (−27.5 kcal mol−1), 4,3-ISOPOOH (−26.9 kcal mol−1) and cis-β-IEPOX (−28.0 kcal mol−1) with the bromide anion, respectively. We find that the formation free enthalpies are almost equal to the value of hypoiodous acid (HOI) clustered with the bromide anion (26.9 kcal mol−1), as presented in ref. 55. Because the instrument used in ref. 55 and in this study is the same and the instrument tuning is identical, the fragmentation of these bromide anion cluster ions should be comparable. He et al.55 calibrated both the H2SO4 and the HOI, and the calibration factor of HOI was approximately two times larger than that of H2SO4. Therefore, the calibration factor used for C5H10O3 is two times the calibration factor for H2SO4 in this study.

    As neither the NH4-PTR3-CIMS nor the Br-MION2-CIMS are able to distinguish between ISOPOOH and IEPOX, the reported C5H10O3 in this study is the sum of ISOPOOH and IEPOX. Earlier studies have shown that ISOPOOH is effectively lost to metal surfaces by converting it to methyl vinyl ketone (MVK) and methacrolein (MACR)42,65,66, whereas IEPOX is not affected by metal surfaces67. However, as the experiments in this study focus on extremely low temperatures (−30 °C and −50 °C), the chamber wall itself may also serve as a cryo-trap68 for both ISOPOOH and IEPOX. Therefore, it prevents us from using wall-loss-rate perturbation experiments to separate these two species at these temperatures.

    To understand the distribution of ISOPOOH and IEPOX in C5H10O3, we carry out a kinetic simulation using the reduced isoprene oxidation mechanism provided in ref. 35. The results are presented in Extended Data Fig. 1b. The simulation is carried out by the F0AM model43. The model requires input parameters such as isoprene, OH, HO2 and O3 concentrations measured by our instruments.

    Another important parameter is the wall-loss rate of IP-OOM. We present an experiment in which we manipulate the loss rate of IP-OOM by turning off the light source and increasing the mixing fan spinning rate from 12% to 100% from the equilibrium conditions in Extended Data Fig. 1a. By turning off the light source, the production of IP-OOM stops. Furthermore, by increasing the fan speed, we increase the maximum wall-loss rate from approximately 1.6 × 10−3 s−1 to 8.5 × 10−3 s−1. The decay rates of C5H10O3 and C5H12O6, with lifetimes of 137 s and 112 s, respectively, are similar to the decay rate of HIO3 (129 s) and also, from previous measurements, H2SO4. Because HIO3 has an accommodation coefficient of unity to the chamber wall, we conclude that C5H10O3 and other species with lower volatilities have similar wall-loss rates. In this study, we apply a general wall-loss rate for these species of 1.6 × 10−3 s−1. This wall-loss rate is calculated from the measured H2SO4 wall-loss rate by correcting the diffusivity of C5H12O6 at −30 °C using the method described by our earlier study58.

    We further conduct simulations for all of our experiments using the same procedure, and the ratio of IEPOX in C5H10O3 versus OH concentration is presented in Extended Data Fig. 1b. As anticipated, the IEPOX ratio is positively correlated with OH concentrations. For further analysis, a fit with an expression of ratio of \({10}^{(0.58\times {\log }_{10}([{\rm{OH}}])-4.6)}\) is plotted.

    Gas-phase oxidized isoprene products

    The gas-phase measurement of IP-OOM was achieved by using a combination of NO3-CIMS, Br-MION2-CIMS and NH4-PTR3-CIMS. As defined in this study, only the species with carbon and oxygen numbers equal to or larger than 4 are considered in the IP0-2N, which are primarily produced from OH oxidation of ISOPOOH and IEPOX with and without involving nitrogen oxides. Furthermore, the particle-phase IP0-2N were monitored by a FIGAERO69, which operates with the bromide chemical-ionization method60 in CLOUD15 (Br-FIGAERO-CIMS) and with the iodide chemical-ionization method in CLOUD16 (I-FIGAERO-CIMS). These chemical-ionization methods exhibit varying preferences for analytes. For example, the NO3-CIMS is renowned for detecting highly oxygenated organic molecules70 that contain more than 5 oxygen atoms. The H3O-PTR-MS is the only one that can detect isoprene, whereas both the NH4-PTR3-CIMS and the Br-MION2-CIMS are capable of detecting semi-volatile organic compounds. Consequently, the combination of these CIMS methods enables the measurement of IP-OOM at different oxidation states.

    It is worth mentioning our specialized approach to measuring IP-OOM using Br-MION2-CIMS during experiments involving excess HONO and/or H2O2, as described previously. In these experiments, the primary ions (Br− and H2OBr−) were substantially transformed into product ions such as HONOBr−, H2O2Br− and (H2O2)2Br−. Consequently, the measurement of IP-OOM could be compromised if HONO and H2O2 strongly bind with Br−, thereby impeding the ligand exchange with IP-OOM. Therefore, we extensively compared the Br-MION2-CIMS measurements with those of NO3-CIMS and NH4-PTR3-CIMS during experiments with and without such primary ion saturation to ensure reliable measurements. We found that the Br-MION2-CIMS measurement remained uncompromised when we included HONOBr−, H2O2Br− and (H2O2)2Br− as the primary ions. This is probably because of the relatively weak bonding of HONO and H2O2 with Br−, which enables effective charging of IP-OOM by allowing ligand exchange reaction. Quantum-chemical calculations further suggest that the formation free enthalpies of HONOBr− and H2O2Br− are −23.6 and −21.2 kcal mol−1, respectively. These numbers are sufficiently lower than other molecules that are detected at the collision limit by Br-MION2-CIMS55.

    To produce IP0-2N, we conducted a set of experiments in which we varied the concentrations of isoprene (ranging from 1.4 × 109 to 4.2 × 1010 cm−3) and OH (ranging from 0.1 to 6.9 × 107 cm−3) to alter the distribution of oxidation products35. To analyse the results of these experiments, we present a generic algorithm to calculate the total sum of gaseous IP0-2N produced, with a focus on those with carbon and oxygen numbers greater than 3:

    1. 1.

      IP-OOM are independently identified by each of the CIMS instruments. Their responses to the isoprene oxidation in the chamber are observed to distinguish them from any background contaminations originating from either the chamber or the ion sources. If an individual IP0-2N is affected by contaminants of the same molecular formula, its background, derived from the nearest cleaning stage, is subtracted from its concentrations.

    2. 2.

      If an IP0-2N is detected by only one of the three CIMS, it is added to the total sum directly.

    3. 3.

      If several CIMS detect species with the same molecular formula, their measured signals are compared in pairs to derive a correlation coefficient. A pair is considered to measure identical molecules if the correlation coefficient is greater than 0.5. However, owing to the transfer of the H2SO4 calibration factors to the measured IP0-2N (NO3-CIMS and Br-MION2-CIMS), the concentration of any molecule with a lower detection efficiency than H2SO4 may be underestimated. The extent of this underestimation depends on the chemical-ionization method used, as the binding enthalpies of the analyte-Br−, analyte-NO3− and analyte-NH4+ may differ. To address this, we add the highest measured concentration of the three CIMS to the IP0-2N and discard the rest, as the highest concentration is probably the closest to the actual concentration.

    4. 4.

      If the correlation coefficient is less than 0.5, we consider that this pair represents two different molecular structures, that is, two isomers or conformers. In this case, both will be added to the IP0-2N.

    However, maintaining all three instruments to be operational throughout all experiments presents a challenge, for instance, the Br-MION2-CIMS operated in the APi-TOF mode to measure charged clusters. Therefore, we excluded data collected during periods in which any one of the instruments was not available.

    Charged clusters

    Naturally charged clusters were measured with two APi-TOF mass spectrometers (Aerodyne Research Inc.) operating at negative and positive ion mode71. The first instrument was equipped with a MION2 operating in the APi-TOF mode (MION2-APi-TOF)55,72 by deactivating the inlet voltages responsible for directing charged reagent ions into the sample flow. The second device was coupled with an ion-molecule reaction chamber (APi-TOF). Overall, the APi-TOF was less sensitive than the MION2-APi-TOF. The charged clusters reported in Fig. 3 were measured with the MION2-APi-TOF, which was validated by the APi-TOF. Because the MION2 inlet was operated in bromide chemical-ionization mode in some experiments, part of the data reported in Extended Data Fig. 7 was measured by the APi-TOF.

    Particle-phase measurements

    We measured the chemical composition of small particles using a FIGAERO coupled to a chemical-ionization mass spectrometer69. Particles were sampled from the CLOUD chamber onto a 5-µm-pore polytetrafluoroethylene (PTFE) filter (MilliporeSigma). Filter mass loading is dependent on particle distribution in the chamber, collection flow rate (typically 7–8 l min−1) and total collection time (1–2 h in this study). After particle collection, the filter was automatically moved to in front of the ion molecule reactor. The filter aligned with a sealed port that constantly flushes pure N2. In CLOUD15, the flow rate during chemical measurement was 3 l min−1 and it was increased to 5 l min−1 in CLOUD16 for more efficient heat transfer in a longer port. Pure N2 was heated from room temperature up to 180 °C using programmed thermal desorption controlled by eyeon software v2.1.4.5. As the filter temperature increased, we detected lower-volatility molecules partitioning back into the gas phase. For the particle filter loadings in this study, we observed that all signals decreased back to the baseline by the end of the heating cycle, indicating no notable remaining mass.

    Typically, FIGAERO-CIMS is operated using I− chemical-ionization in a reduced-pressure ion molecule reactor (about 120–150 mbar). Pure N2 is flowed around a CH3I permeation tube (Vici) and through a 210Po ionizer (NRD LLC) to produce iodide ions. These polarizable ions effectively form adducts with oxygenated organic compounds, with a small fraction of interactions leading to charge transfer between the ion and neutral compound. In CLOUD15, we used Br− chemical-ionization to distinguish between our chemical-ionization reagent and iodine species inside the CLOUD chamber. The set-up is the same as iodide ionization mode except we exchange a CH2Br2 permeation tube and heat it to 40 °C to increase permeation rates. These chemical-ionization techniques are both sensitive to oxygenated organic compounds, organics with nitrate and sulfate functional groups and inorganic acids60,69. Compounds chemically transformed through deprotonation or thermal decomposition have been excluded, as their parent molecule is unknown.

    Particle number size distribution

    The Neutral cluster and Air Ion Spectrometer73,74 (NAIS) was used to measure the naturally charged particle number size distribution from 0.8 to 41 nm and the particle number size distribution (naturally charged + neutral) from 2 to 42 nm in both negative and positive polarities. The nano-condensation nucleus counter was used to measure the particle number size distribution between 1 and 3 nm. It consists of a particle size magnifier75 (PSM, Airmodus Oy). The PSM, which is an aerosol pre-conditioner, uses diethylene glycol to grow aerosol particles as small as 1 nm to sizes that can be easily detected by a CPC75. Furthermore, a butanol CPC (TSI 3776) was used to measure the total number concentration of particles with diameters greater than 2.5 nm. A nano-scanning mobility particle sizer (TSI 3938)76 coupled to a butanol CPC (TSI 3776), was used to measure the particle-size distribution within the range 6–65 nm, whereas particles larger than 65 nm were measured using a commercially available long-SMPS (TSI 3082) coupled to a water butanol CPC (TSI 3775).

    Yield of IP0N from ISOPOOH

    As shown in a previous section, the IP0N in this study is defined as species with C,O ≥ 4. Therefore, ISOPOOH and IEPOX are not included in the IP0N. ISOPOOH and IEPOX are treated as the direct precursors of IP0N, which in turn contribute to isoprene new particle formation. It is worth noting that both ISOPOOH and IEPOX undergo oxidation, producing compounds with C,O ≥ 4. However, the reaction rate of ISOPOOH is approximately ten times larger than IEPOX35. To account for the difference in reaction-rate coefficients, we predict the ratio of IEPOX in C5H10O3 using the data shown in Extended Data Fig. 1b based on the OH concentrations. Assuming that the concentration of IP0N is at equilibrium, the primary mechanism for IP0N loss is wall deposition, which is approximately equal to the production of IP0N from ISOPOOH and IEPOX. Therefore,

    $$\begin{array}{l}[{{\rm{IP}}}_{0{\rm{N}}}]\times {k}_{{\rm{wall}}}\,=\,R\times ({k}_{{\rm{OH}} \mbox{-} {\rm{ISOPOOH}}}\times [{\rm{OH}}]\times [{\rm{ISOPOOH}}]\\ \,\,\,\,\,\,+{k}_{{\rm{OH}} \mbox{-} {\rm{IEPOX}}}\times [{\rm{OH}}]\times [{\rm{IEPOX}}])\end{array}$$

    in which kOH-ISOPOOH and kOH-IEPOX are the reaction-rate coefficients of ISOPOOH (10−10 cm3 s−1) and IEPOX (10−11 cm3 s−1) with OH (ref. 35), respectively; [IP0N], [OH], [ISOPOOH] and [IEPOX] show concentrations and kwall is the wall-loss rate of C5H12O6; R represents the yield of IP0N from C5H10O3.

    We then define the reacted C5H10O3 (cm−3) as:

    $${\rm{Reacted}}\,{{\rm{C}}}_{5}{{\rm{H}}}_{10}{{\rm{O}}}_{3}=\frac{{k}_{\text{OH-ISOPOOH}}\times [{\rm{OH}}]\times [{\rm{ISOPOOH}}]+{k}_{\text{OH-IEPOX}}\times [{\rm{OH}}]\times [{\rm{IEPOX}}]}{{k}_{{\rm{wall}}}}$$

    The yield of IP0N from reacted C5H10O3 is depicted in Extended Data Fig. 2. We find that the yields of IP0N are approximately 46% at −30 °C and 55% at −50 °C. However, it is essential to note that the detection of C5H10O3, IP0N and OH has various uncertainties. We estimate that the derived yield has an uncertainty of at least a factor of two, with the quantification of IP0N being the main source of uncertainty.

    One further source of error in determining the yield is the contribution of highly oxygenated molecule production from the first-generation isoprene hydroxy peroxy radical (ISOPOO, C5H9O3) through auto-oxidation or dimer formation. For example, the reaction between two ISOPOO radicals can generate C10H18O4, and intramolecular H-shift followed by HO2 termination of ISOPOO produces C5H10O5. Although these two molecules only contribute to a small fraction of IP0N in this study, other similar channels may contribute to a greater extent to IP0N, thereby reducing the yield of IP0N from C5H10O3. As disentangling first-generation and second-generation highly oxygenated molecules from isoprene oxidation is not the objective of this study, future research is necessary to investigate this direction.

    Quantum-chemical calculations

    Quantum-chemical methods are used to compute cluster formation free enthalpies and proton affinities. Initially, the Spartan’18 program is used for the conformational sampling with the MMFF method. Subsequently, density function theory (DFT) methods are used to optimize the molecules first at the B3LYP/6-31+G(d) level of theory, followed by optimization and frequency calculations at the ωB97X-D/aug-cc-pVTZ-PP level of theory77,78 on conformers within 2 kcal mol−1 in relative electronic energies. Bromine pseudopotential definitions are obtained from the Environmental Molecular Sciences Laboratory (EMSL) basis set library79,80. The DFT calculations are carried out using the Gaussian 16 program81. To refine the DFT-calculated enthalpies, an extra coupled-cluster single-point energy correction is performed at the DLPNO-CCSD(T)/def2-QZVPP level of theory on the lowest-energy conformers. This coupled-cluster calculation is conducted using the ORCA program version 5.0.3 (ref. 82).

    Calculation of the nucleation and growth rates

    The nucleation rate, J1.7, is calculated on the basis of PSM measurement of particles at a mobility diameter of 1.7 nm (1.4 nm in physical diameter83), which are generally considered to be larger than their critical cluster sizes and thus stable.

    To determine the nucleation rates, the time evolution of the particle concentration is analysed, taking into account various loss processes that also affect the concentration. However, because loss processes in a chamber setting differ from those in the atmosphere, the calculation method must be adjusted for chamber experiments84. Specifically, the nucleation rate (J1.7) is calculated by factoring in losses specific to the CLOUD chamber, such as dilution, wall and coagulation losses. We calculated Jdp as follows:

    $${J}_{1.7}=\frac{{\rm{d}}N}{{\rm{d}}t}+{S}_{{\rm{dil}}}+{S}_{{\rm{wall}}}+{S}_{{\rm{coag}}}$$

    in which dN/dt is the time derivative of the total particle concentration above a certain particle size (here >1.7 nm for J1.7) and Sdil, Swall and Scoag are the particle losses owing to dilution, wall and coagulation. The details can be found in ref. 84. To calculate the coagulation sink, we used the combined particle-size distribution from three instruments (NAIS, nano-SMPS and long-SMPS).

    Furthermore, the nucleation rate at 2.5 nm, J2.5, derived from the butanol CPC and corrected by the same method described above, is calculated. The results are presented in Extended Data Fig. 6 in the same format as in Fig. 2. Because the CPC was not affected by the systematic upgrade in the cooling system between CLOUD15 and CLOUD16, it serves to distinguish subtle changes in our data. For example, the nucleation rates from experiments with NOx (filled squares in Fig. 2) seem to be similar to the experiments without NOx (filled circles in Fig. 2). This is probably a result of systematic errors introduced by changing either the cooling system or the instrument-calibration experiments. On the other hand, Extended Data Fig. 6 shows that experiments with NOx have nucleation rates higher than the experiments without NOx, therefore, isoprene nitrates (IP1-2N) do contribute, despite to a lesser extent compared with IP0N, to particle nucleation.

    To calculate particle growth rates, we use the 50% appearance-time method, as outlined in previous studies58,84,85. It is worth noting that the appearance-time method can overestimate growth rates when the impacts of coagulation (coagulation sink, coagulation source and particle coagulation growth) are non-negligible compared with the condensation growth, but the coagulation impact is rather small in the CLOUD experiments. For a deeper understanding of the molecular-level theory behind the method, we refer to the theoretical derivation presented in ref. 58. The particle number size distribution data used to calculate growth rates between 3.2 and 8.0 nm are measured by the NAIS. During previous experiments with α-pinene and sulfuric acid, we have confirmed that the growth rates measured with the NAIS in total mode are similar to those measured with the DMA-train86.

    Comparison of experimental and ambient conditions

    To compare the CLOUD experimental conditions with ambient conditions in the tropical upper troposphere, we summarize in Extended Data Table 1 the key chemical and physical parameters of the CLOUD experiments and the CAFE-Brazil (CB) flight campaign13. The CLOUD statistics are summarized from all experiments presented in this study, separated into two temperature conditions at −30 °C and −50 °C, respectively. Statistics of the CB flight campaign are derived from a single research flight, RF 19, samples T4 and T9, shown in Fig. 1 of ref. 13. All vapour concentrations are presented in units of molecules per cm−3—the quantities as measured—for both CLOUD and CB. The values from the CB campaign are not corrected to their values at standard temperature and pressure, to allow for a direct comparison of the chemical and aerosol formation kinetics between the CLOUD experiments and the flight measurements. We elaborate below on three aspects of this comparison: (1) isoprene non-nitrates (IP0N) and nitrates (IP1-2N); (2) atmospheric acids; and (3) impact of atmospheric pressure on particle nucleation.

    Distribution of IP0N and IP1-2N

    In general, the CLOUD experiments were designed to mimic ambient conditions as closely as possible. Key parameters such as temperature, relative humidity (RH), isoprene, O3, NO and HO2/OH ratios are directly comparable between the CLOUD experiments and the CB measurements. The largest differences between CLOUD and CB are higher atmospheric pressure in CLOUD and higher OH/HO2 concentrations, resulting in a higher HO2/NO ratio in CLOUD. The higher OH concentration in the CLOUD chamber is required to reproduce ambient IP-OOM concentrations at a chamber-wall-loss rate of approximately 2 × 10−3 s−1. In the upper troposphere, the condensation sink for low-volatility gaseous species could be several times or even up to one order of magnitude lower than the chamber-wall-loss rate.

    Because this study aims to investigate the contribution of IP0N and IP1-2N to particle nucleation and growth, these parameters are critical for CLOUD to reproduce at atmospheric concentrations. A consequence of the relatively higher HO2/NO ratio in CLOUD is that the IP0N to IP1-2N ratio is elevated compared with CB. However, because the higher operating pressure in CLOUD favours the formation of IP1-2N by accelerating the reaction of organic peroxy radical (RO2) with NO as well as enhancing the organic nitrate formation branching ratio35 (see Extended Data Table 1), this effect largely compensates for the higher HO2/NO ratio in CLOUD. Nevertheless, regardless of the chemical details, which will be presented by follow-up studies, CLOUD successfully reproduces isoprene oxidation products IP0N and IP1-2N, in terms of both absolute values and relative ratios (Extended Data Table 1). The wide range of IP0N and IP1-2N concentrations and IP0N/IP1-2N ratios covered by CLOUD experiments enables CLOUD to reasonably simulate particle nucleation and growth dynamics, consistent with CB measurements during dawn hours (T4 period in Extended Data Table 1) and morning (T9 period in Extended Data Table 1). As the daylight hours proceed beyond the period measured by CB, both OH and HO2 concentrations will increase, whereas NOx concentrations decrease, favouring the formation of IP0N over IP1-2N. Thus, the importance of IP0N may be further enhanced after noon. It is also noteworthy that chemical distribution and nucleation dynamics might differ in other seasons and locations from those covered by CB measurements, such as the cases presented in Fig. S11 of ref. 5. Therefore, we believe that the wide range of conditions explored by CLOUD provides valuable data to enable global models to evaluate the impact of isoprene on new particle formation in other upper-tropospheric environments in which NOx concentrations may differ from those measured by CB.

    Impact of sulfuric acid and iodine oxoacids

    In this study, we observe a large enhancement of IP-OOM nucleation rates from trace amounts of atmospheric acids (specifically, H2SO4 and HIOx). The enhancement starts at acid concentrations of 105 cm−3 and, at an acid concentration of 2 × 106 cm−3, the particle-nucleation rate is approximately 100-fold faster than without added acids.

    The CB measurements are unable to comment on this synergistic role of acids for IP-OOM particle nucleation owing to their H2SO4 detection limit of several times 106 cm−3. Nevertheless, acid enhancement of IP-OOM nucleation can be expected to occur in the atmosphere. Aircraft measurements indicate that approximately 10 pptv SO2 is ubiquitous in the global atmosphere between the marine boundary layer and the upper troposphere87,88. Global simulations also suggest that H2SO4 concentrations greater than 105 cm−3 are widespread throughout the troposphere44. The global distribution of HIOx is less well known and global simulations and aircraft measurements are needed to quantify its concentrations in the upper troposphere. However, recent measurements of iodine oxide and particle-phase iodine in the upper troposphere89 suggest that HIOx may also play a role in enhancing IP-OOM particle nucleation.

    Impact of atmospheric pressure on particle nucleation

    Cluster-forming interactions (such as nucleation) are of the form:

    $$\begin{array}{ll}{\rm{A}}+{\rm{B}}\to {{\rm{C}}}^{* } & ({\rm{R}}1\,;\,{\rm{condensation}}),\\ {{\rm{C}}}^{* }\to {\rm{A}}+{\rm{B}} & ({\rm{R}}2\,;\,{\rm{evaporation}})\,{\rm{and}}\\ {{\rm{C}}}^{* }+{\rm{M}}\to {\rm{C}}+{\rm{M}} & ({\rm{R}}3\,;\,{\rm{thermalization}}),\end{array}$$

    in which A and B are two molecules (or small clusters) forming the cluster C and C* is a vibrationally excited state of the cluster containing the cluster energy EAB as part of its internal vibrational energy. The excited cluster C* will lose energy to the bath gas, M, with a concentration given by pressure through the ideal gas law. Cluster-forming interactions can therefore depend on ambient pressure90. When pressure is relatively ‘low’, reaction R3 will be the rate-limiting step for cluster formation and the overall rate will be of the third order (in A, B and M). However, when pressure is relatively high, reaction R1 will be the rate-limiting step and the rate will be of the second order (in A and B) and be independent of pressure, that is, at the so-called ‘high-pressure limit’. The critical pressure occurs when the rates of R2 and R3 are equal and therefore depends on the lifetime (evaporation rate) of C* through reaction R2, as well as the ambient pressure through reaction R3.

    In practice, only systems with very few heavy atoms (four or fewer heavy atoms, in which ‘heavy’ excludes hydrogen) would show any meaningful pressure dependence in the atmosphere91, as quantified below. Because IP-OOM nucleation typically involves more than 15 heavy atoms, measurements in the CLOUD chamber at near 1 bar are directly applicable to the upper troposphere near 0.2 bar, provided the results are interpreted in terms of number concentrations (thus accounting for the dilution effect of reduced pressure) and not mixing ratios.

    The (microcanonical) decomposition rates (inverse lifetimes) of the cluster are given by Rice–Ramsperger–Kassel–Marcus theory90. It depends strongly on the number of internal vibrational modes in C*. As a rule, it can be estimated (by Rice, Ramsperger and Kassel theory)90 roughly as the ‘fractional excess free energy’, \(\upsilon {\left(\frac{e-{e}_{0}}{e}\right)}^{s}\), in which υ is a typical frequency, 100 THz or so, e is the cluster energy above the ground vibrational state and e0 is the critical energy for decomposition (the cluster energy EAB) and s is an effective number of vibrational modes in the cluster. Approximately, e − e0 will be on the order kT (200 cm−1), whereas for the systems nucleating under atmospheric conditions, e0 will be on the order 800 cm−1 or more. Thus, e will be on the order 1,000 cm−1 and (e − e0)/e will be on the order 0.2. Again, approximately and conservatively, s is 3N − 7, in which N is the number of heavy (non-H) atoms in C*. The ‘7’ excludes external modes as well as the reaction coordinate. A system with five heavy atoms would have a decay coefficient of roughly 2.6 × 108 s−1, whereas the collision frequency at 1 atm is near 1010 s−1. Such a system would (barely) show some pressure dependence. By contrast, for isoprene oxidation products and H2SO4, N is probably 15, so s = 3N − 7 = 38. Given this, the microcanonical decay coefficients for these clusters will be on the order 3 × 10−13 s−1. This is extremely slow. In practice, it means that the energy distribution in cluster C will be entirely thermal, that is, given by a Boltzmann term at the ambient temperature, and the rates of cluster formation (and decomposition or evaporation) will be unaffected by pressure.

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  • Why the EU must reset its Green Deal – or be left behind

    Why the EU must reset its Green Deal – or be left behind

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  • European Commission. The European Green Deal: Delivering the EU’s 2030 Climate Targets (EC, 2023).


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  • Chocolate Has a Sustainability Problem. Science Thinks It’s Found the Answer

    Chocolate Has a Sustainability Problem. Science Thinks It’s Found the Answer

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    Elsewhere, Mars is looking to get to the literal root of the problem by improving the resilience of the all-important cocoa plant. The food giant is working with the USDA and UC Davis to genome sequence pathogens for the diseases wreaking havoc on crop yields, including black pod disease. It hopes that by understanding the problems on a microscopic level, it can select resilient cacao trees and bypass the sector’s supply headaches altogether.

    Nag points to other areas of development, which focus on improving the quality of new solutions. In particular, she suggests that pascalization may hold promise.

    “Pascalization [also referred to as high-pressure processing—HPP] involves applying high levels of hydrostatic pressure to cocoa products to stabilize cocoa particles and prevent the separation of cocoa powder,” she explains.

    “This technique preserves flavors and nutrients, extends shelf life, modifies texture, and ensures food safety in cocoa and chocolate products without relying on heat or chemical preservatives. While this method is still under research, it shows promise for enhancing the texture of chocolate products, particularly in alternative formulations.”

    Regardless of the growing competition, Mishra is confident in the full pod potential. However, his team isn’t the first to consider it, and both Nestle and Lindt & Sprüngli have made tentative inroads into similar markets, with varying degrees of success.

    After launching its all-cocoa product Incoa in 2019, Nestlé quietly retracted it from the market in 2023 after it received a disappointing reception from a select few European markets. The chocolate did not use the endocarp, and skipped the gel-making stage, but had promised similar positive outcomes for farmers. Elsewhere, Lindt & Sprüngli apparently found more appetite following the launch of its Cocoa Pure product in 2021; it continues to offer the limited edition 100 percent cocoa bar, also in partnership with Koa—but also only using the pulp.

    The industry spirit appears to be open to new ideas, then, but would the public embrace this new chocolate, and will ETH Zurich’s unique chocolate-making method ever make it out of the lab?

    “If I didn’t have a daytime job, I would probably start a company,” says Mishra. “But the true milestone for implementation that has to be achieved is for a chocolate company to take the risk of prototyping a product—an actual product, not a product done by scientists. We scientists are really bad at making culinary delights, typically. I think as soon as a bigger chocolate manufacturer deems it a worthy path to go down, change will begin.”

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  • Returning the Amazon Rainforest to Its True Caretakers

    Returning the Amazon Rainforest to Its True Caretakers

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    In 2025, a small, indigenous nation that calls itself the “people of many colors” will go home for the first time in 80 years. Their return will drive a movement of indigenous peoples across the Amazon rainforest fighting for legal titles to their ancestral territories, and winning. These victories will have global significance.

    The Siekopai lived for centuries along what is now the border between Ecuador and Peru in the western Amazon. In the 1500s, they were a powerful civilization with their own unique varieties of corn and an army capable of defeating the Portuguese conquerors and stopping their advance. Later, however, they were decimated by disease, enslaved by rubber tappers, and forcibly relocated to Jesuit missions. Approximately 80 years ago, a war between Ecuador and Peru displaced the remaining Siekopai. When the years of conflict waned, in 1979, a new, if contested, border cut through their homelands. The Siekopai now number about 1,950 survivors, with 750 in Ecuador and 1,200 in Peru.

    In Ecuador, indigenous nations are in a landlord-tenant agreement with the Ministry of the Environment. There are now nearly 5 million acres of indigenous rainforest territories locked in “protected areas” within the Ministry of Environment’s control. This gives the government, for instance, the power to grant drilling rights, as it did in the Yasuní National Park, or to change the nature of the tenant agreement, which they did when the Cuyabeno Wildlife Reserve was created, denying indigenous people the right to hunt, fish, or garden and effectively making them trespassers in their own land.

    In Peru, the government leases land to indigenous communities indefinitely for various uses based on the type of soil. Only 20 percent of the indigenous area is recognized as Siekopai property, while the remaining 80 percent is designated as state-owned forest lands, and are “on loan” from the state.

    Recently, however, the Siekopai have successfully challenged the legality of these titling laws—the legal process that results in the recognition of the right to property of indigenous people to their ancestral lands—and have already won two major legal victories in Ecuador and Peru. In 2021, the Siekopai received land titles to more than 500,000 acres of their lands in Peru. In September 2022, the Siekopai filed a suit against the government of Ecuador to regain ownership over Pë’këya, part of their ancestral territory located along the border. In November 2023, an Ecuadorian appeals court ruled in favor of the Siekopai, granting them legal title to another 100,000 acres of labyrinthine flooded forests and blackwater lagoons in the heart of their ancestral homelands, and marking the first time the government would issue land title to an indigenous peoples whose territory was located inside a protected area.

    In 2025, working together with Amazon Frontlines and the Ceibo Alliance—allied organizations with the mission to protect both the headwaters of the Amazon rainforest and indigenous autonomy—the Siekopai will further expand their land titles and create a pathway to permanently protect nearly 5 million acres of rainforest within national parks in Ecuador. In Peru, they’re going to dismantle the legal and political barriers to titling an estimated 40 million acres of ancestral indigenous territory in the Amazon. These landmark victories will set a legal precedent for millions of other indigenous people across the Amazon and hopefully allow them to return to their ancestral lands.

    Permanent land titles are not only essential to the survival of indigenous lives and cultures. They are also crucial to our collective ability to protect the rainforest. The Amazon rainforest is approaching a tipping point from which it may never recover. Between 1985 and 2022, people burned or cut down more than 11 percent of the Amazon, an area larger than France and Uruguay combined. If this rate of deforestation continues, the entire rainforest will be doomed. By 2050, the entire region could be irreversibly on the path to becoming a savanna. The destruction of the Amazon is, at the same time, the destruction of more than 300 distinct ethnicities. In other words: It is mass ecocide and ethnocide.

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  • Where in the world is there potential for tropical-forest regeneration?

    Where in the world is there potential for tropical-forest regeneration?

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    • RESEARCH BRIEFINGS

    To restore tropical forests at scale requires cost-effective methods. An estimated 215 million hectares — an area larger than that of Mexico — have potential for natural forest regeneration, which could lead to an estimated above-ground sequestration of 23.4 gigatonnes of carbon over 30 years.

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  • How the World Can Cope Better With Extreme Rainfall and Flooding

    How the World Can Cope Better With Extreme Rainfall and Flooding

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    According to Bodoque, we need to improve flood-risk mapping too. There is a need to characterize vulnerability holistically, which implies considering the social, economic, physical, institutional, and cultural dimensions of what makes a community vulnerable to the weather. It is necessary to understand all components of what heightens people’s risk: not just their exposure to extreme weather, but how sensitive they are to it, and how resilient. Bodoque’s own research has found that most of the literature on vulnerability to natural disasters usually considers only two dimensions—the social and economic—with institutional and cultural qualities of regions being neglected.

    As for the challenges of integrating flood-prone area mapping into regional decisionmaking, Bodoque points out that in the European Union there is a regulatory framework that includes a preliminary flood risk assessment, as well as hazard maps in which risk must be calculated according to the population and exposed assets. “There is quite a lot of room for improvement; the flood hazard maps present quite a lot of uncertainty.” In part, he explains, this is because flooding is a random process. It is very likely that where an intense flood has already occurred, another one will occur later, but it is not possible to know if it will happen in five or 300 years.

    In addition to this, Bodoque explains, there is another issue. The parameters that feed the risk maps are not fixed values, but ranges—you can feed in upper, middle, or lower values, as desired. Yet the maps used in Spain and many other countries are deterministic; that is, they indicate only floodable and nonfloodable areas. In other words, they only see black and white. “I am providing a single cartographic output, when for each of the parameters and for range I have infinite outputs,” Bodoque says. Uncertainty is flattened into a deterministic map that can then generate a false sense of security.

    It is necessary, Bodoque says, to change this method of generating maps that represent the probabilities of risk in flood-prone areas. This approach would better reflect the uncertainty inherent in flood events. However, this probabilistic model entails a high computational cost.

    To better address the risks associated with torrential rains, Bodoque stresses the importance of making the population aware of the danger they face. In Spain, he and his colleagues have found that people exposed to natural weather processes do not perceive that they are at risk, partly because extreme weather events do not occur every year.

    This low perception of risk has deadly consequences, as it encourages imprudent decisions in risky situations. Against this, Bodoque suggests developing communication plans for different audiences. In an article published in the Journal of Hydrology, of which he is a coauthor, Bodoque indicates that while “risk management based on a technocratic approach can give people a false sense of security,” the implementation of a good risk-communication strategy would facilitate a better response to emergency alerts.

    This story originally appeared on WIRED en Español and has been translated from Spanish.

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  • Climate resilience must be integrated into UN Sustainable Development Goals

    Climate resilience must be integrated into UN Sustainable Development Goals

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    A new study reveals that climate change, the most pressing threat to global socio-economic development and the environment, demands a reimagining of the United Nations Sustainable Development Goals (SDGs).

    The University of Birmingham’s research underscores the need for an integrated approach to infuse climate resilience into every facet of the SDGs. Experts argue this is the only viable path to securing a sustainable future for our planet.

    What are the UN Sustainable Development Goals?

    Adopted in 2015, the 17 UN Sustainable Development Goals represent a global agenda to address urgent challenges by 2030.

    These goals, established through extensive consultations with nations, organisations, and civil society, aim to:

    1. End poverty in all its forms everywhere.
    2. Eradicate hunger, improve food security, and promote sustainable agriculture.
    3. Ensure healthy lives and well-being for all ages.
    4. Guarantee equitable, inclusive education and lifelong learning opportunities.
    5. Achieve gender equality and empower women and girls.
    6. Provide access to clean water and sanitation.
    7. Ensure access to affordable, reliable, and sustainable energy.
    8. Promote sustainable economic growth and decent work for all.
    9. Build resilient infrastructure and foster innovation.
    10. Reduce inequality within and among countries.
    11. Create sustainable cities and communities.
    12. Ensure sustainable consumption and production patterns.
    13. Take urgent action to combat climate change and its impacts.
    14. Conserve and sustainably use marine resources.
    15. Protect and restore terrestrial ecosystems.
    16. Promote peaceful, inclusive societies with access to justice for all.
    17. Strengthen global partnerships for sustainable development.

    These interlinked goals strive to create a world free of poverty and inequality while fostering environmental stewardship. Yet, climate change challenges their implementation.

    Lead author Dr Ajit Singh added: “Embedding climate action within each SDG would ensure that climate resilience is a core component of sustainable development.

    “If we fail to resolve tensions between development goals and climate action, we will find it impossible to secure the future of our planet and its people.”

    Climate action: The key to SDG success

    Following discussions at COP29 in Baku, University of Birmingham researchers argue that integrating climate resilience into the SDGs is vital.

    The interdisciplinary team has developed a five-point action plan to align climate goals with the SDG framework. Key recommendations include:

    1. Harmonising the Paris Agreement and SDGs: Establish a cohesive roadmap for sustainable development.
    2. Setting short- and long-term targets: A structured approach ensures immediate action alongside future planning.
    3. Empowering local communities: Locals must play a central role in creating and executing climate policies.
    4. Developing unified financial systems: Focus on climate resilience, especially in vulnerable regions.
    5. Creating an international climate panel: Foster knowledge exchange and collaboration across sectors.

    Why climate resilience matters for development

    The study highlights how climate change exacerbates poverty, disrupts health systems, and widens inequality.

    Its impact on agriculture threatens food security, while its effects on water ecosystems endanger marine biodiversity.

    Moreover, climate disasters disproportionately harm vulnerable communities, emphasising the need for localised, inclusive strategies.

    Experts emphasise that climate education should be embedded within school curricula to equip future generations with the tools to address environmental challenges.

    Climate-resilient policies, such as sustainable agriculture and ocean conservation, are also critical to mitigating damage.

    A global responsibility

    Although the UN Sustainable Development Goals were crafted collaboratively, their implementation lies in the hands of individual countries.

    This decentralised approach demands robust international cooperation to ensure success. Aligning climate action with the UN SDGs not only addresses environmental threats but also promotes equitable social and economic progress.

    The call to action is clear: without integrating climate resilience into the UN Sustainable Development Goals, humanity risks undermining its progress on poverty eradication, health equity, and global prosperity.

    As the clock ticks closer to 2030, urgent, unified efforts are essential to transform these aspirations into a sustainable reality.

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  • Ocean acidification is reaching deeper waters

    Ocean acidification is reaching deeper waters

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    Deep-sea coral reefs are at risk from acidification

    Howard Chew / Alamy Stock Photo

    Ocean acidification is sinking into marine regions as deep as 1500 metres, posing new threats to organisms like sea butterflies, sea snails and cold-water corals.

    The ocean is the largest natural sink of carbon dioxide, absorbing about a quarter of our annual emissions. That uptake of CO2 makes the ocean’s surface more acidic, with consequences for sensitive ecosystems like coral reefs. But until now, researchers did not know the extent to which acidification was reaching deeper waters.

    Jens Daniel Müller at the Federal Institute of Technology Zurich in Switzerland and his colleagues developed a 3D reconstruction of how CO2 moves through the ocean, based on global measurements of currents and other circulation patterns. They used this model to estimate how the carbon dioxide the oceans have absorbed since 1800, around the start of the industrial revolution, has affected deep-water acidity.

    They found a clear acidification signal down to 1000 metres in most of the ocean. Some areas, such as the North Atlantic – where the powerful Atlantic meridional overturning current (AMOC) carries carbon from the surface to deeper waters – saw acidification down to 1500 metres. Some pockets of deeper water that are naturally more acidic saw even more acidification than the surface. Their higher original acidity reduces their capacity to absorb any added CO2, says Müller.

    This is more or less what researchers expected would happen as the ocean takes up more CO2, says Hongjie Wang at the University of Rhode Island. “But it’s a different thing to really see the data coming in to affirm this.”

    Notably, more than half of all the acidification since 1800 occurred after 1994, as our emissions of CO2 have risen exponentially. “We see this rather rapid progression,” says Müller.

    The magnitude of the acidification is enough to threaten the survival of organisms in large areas of the ocean. Pteropods like sea snails and sea butterflies are at particular risk because they build their shells out of calcium, which dissolves if the water gets too acidic. The rise in acidification has also doubled the areas where cold-water corals will have trouble surviving.

    And ocean acidification is set to continue as the water absorbs more CO2. “Even if we were able to stop CO2 emissions immediately, we would still – for a couple of hundred of years or so – see a process of ocean acidification in the interior,” says Müller.

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  • Images reveal how climate change is upending life in Morocco’s oases

    Images reveal how climate change is upending life in Morocco’s oases

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    Morocco - Tafilalt Oasis - On July 2019 a wildfire spread over 3km across the palm grove burning more than 10,000 trees of which 2500 were palm trees. Summer wildfires are due to a combination of extreme temperatures and lying dead trees. which can easily catch fire in the summer months.

    Date palms

    Matilde Gattoni

    The world’s oases are at the forefront of an existential battle against climate change: limited rainfall and rising heat have dramatically affected these unique ecosystems and the culture they sustain. Morocco has lost two-thirds of its oases – lush, fertile areas in the desert – in just a single century.

    Morocco - M?hamid - Traditional amazigh musicians walk in the desert while performing a traditional rain chant . The amazigh culture is oral and music plays a big part in transmitting the cultural heritage of the tribe. They sing their love for the desert and recount the days when they were nomads.

    Local people plead with the desert for water

    Matilde Gattoni

    Take the town of M’Hamid El Ghizlane, the last stop before the vast, dry expanse of the Sahara. Here, local people plead with the desert for water (pictured above). Dressed in white robes, they regularly meet at the edge of the desert to recite ancestral chants asking for an end to the drought and for life to be brought back to the land.

    While droughts have always been part of life here, they used to be intermittent, allowing people to stock food and water to make it through dry times. But the oasis that sustains the community has shrunk over the past few decades, leading to scorched palm trees and threatening centuries of culture and tradition.

    Morocco - M?hamid - A villager feeds his camel with herbs picked in the dry river bed of the Draa.

    A villager feeds his camel with herbs picked in the dry river bed of the Draa.

    Matilde Gattoni

    The town’s economy has traditionally been sustained by date palms (main picture) and camel herding (pictured above), but with those livelihoods in jeopardy, many are relocating to nearby cities. Those who remain often earn a living through tourism. Former farmers turned self-taught guides offer visitors desert expeditions and tea ceremonies (pictured below) – a glimpse of the life that persists despite the challenges.

    Morocco - Kasr Bounou - Mina el Bouni, around 55, preparing tea with herbs. Mina left her family house in 2008 after it was covered with sand dunes and now lives in her neighbour???s house with her family. Kasr Bounou has lost most of its inhabitants due to the desertification, only four families still live there.

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