Chaperone Machineries of Rubisco - The Most Abundant Enzyme.

A major challenge faced by human civilization is to ensure that agricultural productivity keeps pace with population growth and a changing climate. All food supply is generated, directly or indirectly, through the process of photosynthesis, with the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) catalyzing the assimilation of atmospheric CO2. Despite its pivotal role, Rubisco is a remarkably inefficient enzyme and must be made by plants in large quantities. However, efforts to enhance Rubisco performance by bioengineering have been hampered by its extensive reliance on molecular chaperones and auxiliary factors for biogenesis, metabolic repair, and packaging into membraneless microcompartments. Here, we review recent advances in understanding these complex machineries and discuss their implications for improving Rubisco carboxylase activity with the goal to increase crop yields.

Ectomycorrhizal trees rely on nitrogen resorption less than arbuscular mycorrhizal trees globally.

Nitrogen (N) resorption is an important pathway of N conservation, contributing to an important proportion of plant N requirement. However, whether the ratio of N resorption to N requirement may be affected by environmental factors, mycorrhizal types or atmospheric CO2 concentration remains unclear. Here, we conducted a meta-analysis on the impacts of environmental factors and mycorrhizal types on this ratio. We found this ratio in ectomycorrhizal (EM) trees decreased with mean annual precipitation, mean annual temperature, soil total N content and atmospheric CO2 concentration and was significantly lower than that in arbuscular mycorrhizal (AM) trees. An in situ 15 N tracing experiment further confirmed that AM trees have a stronger reliance on N resorption than EM trees. Our study suggests that AM and EM trees potentially have different strategies for alleviation of progressive N limitation, highlighting the necessity of incorporating plant mycorrhizal types into Earth System Models.

Regulatory NADH dehydrogenase-like complex optimizes C4 photosynthetic carbon flow and cellular redox in maize.

C4 plants typically operate a CO2 concentration mechanism from mesophyll (M) cells into bundle sheath (BS) cells. NADH dehydrogenase-like (NDH) complex is enriched in the BS cells of many NADP-malic enzyme (ME) type C4 plants and is more abundant in C4 than in C3 plants, but to what extent it is involved in the CO2 concentration mechanism remains to be experimentally investigated. We created maize and rice mutants deficient in NDH function and then used a combination of transcriptomic, proteomic, and metabolomic approaches for comparative analysis. Considerable decreases in growth, photosynthetic activities, and levels of key photosynthetic proteins were observed in maize but not rice mutants. However, transcript abundance for many cyclic electron transport (CET) and Calvin-Benson cycle components, as well as BS-specific C4 enzymes, was increased in maize mutants. Metabolite analysis of the maize ndh mutants revealed an increased NADPH : NADP ratio, as well as malate, ribulose 1,5-bisphosphate (RuBP), fructose 1,6-bisphosphate (FBP), and photorespiration intermediates. We suggest that by optimizing NADPH and malate levels and adjusting NADP-ME activity, NDH functions to balance metabolic and redox states in the BS cells of maize (in addition to ATP supply), coordinating photosynthetic transcript abundance and protein content, thus directly regulating the carbon flow in the two-celled C4 system of maize.

Half of the 18O enrichment of leaf sucrose is conserved in leaf cellulose of a C3 grass across atmospheric humidity and CO2 levels.

The 18O enrichment (Δ18O) of cellulose (Δ18OCel) is recognized as a unique archive of past climate and plant function. However, there is still uncertainty regarding the proportion of oxygen in cellulose (pex) that exchanges post-photosynthetically with medium water of cellulose synthesis. Particularly, recent research with C3 grasses demonstrated that the Δ18O of leaf sucrose (Δ18OSuc, the parent substrate for cellulose synthesis) can be much higher than predicted from daytime Δ18O of leaf water (Δ18OLW), which could alter conclusions on photosynthetic versus post-photosynthetic effects on Δ18OCel via pex. Here, we assessed pex in leaves of perennial ryegrass (Lolium perenne) grown at different atmospheric relative humidity (RH) and CO2 levels, by determinations of Δ18OCel in leaves, Δ18OLGDZW (the Δ18O of water in the leaf growth-and-differentiation zone) and both Δ18OSuc and Δ18OLW (adjusted for εbio, the biosynthetic fractionation between water and carbohydrates) as alternative proxies for the substrate for cellulose synthesis. Δ18OLGDZW was always close to irrigation water, and pex was similar (0.53 ± 0.02 SE) across environments when determinations were based on Δ18OSuc. Conversely, pex was erroneously and variably underestimated (range 0.02-0.44) when based on Δ18OLW. The photosynthetic signal fraction in Δ18OCel is much more constant than hitherto assumed, encouraging leaf physiological reconstructions.

Elevated CO2 concentration increases maize growth under water deficit or soil salinity but with a higher risk of hydraulic failure.

Climate change presents a challenge for plants to acclimate their water relations under changing environmental conditions, and may increase the risks of hydraulic failure under stress. In this study, maize plants were acclimated to two different CO2 concentrations ([CO2]; 400 ppm and 700 ppm) while under either water stress (WS) or soil salinity (SS) treatments, and their growth and hydraulic traits were examined in detail. Both WS and SS inhibited growth and had significant impacts on hydraulic traits. In particular, the water potential at 50% loss of stem hydraulic conductance (P50) decreased by 1 MPa in both treatments at 400 ppm. When subjected to elevated [CO2], the plants under both WS and SS showed improved growth by 7-23%. Elevated [CO2] also significantly increased xylem vulnerability (measured as loss of conductivity with decreasing xylem pressure), resulting in smaller hydraulic safety margins. According to the plant desiccation model, the critical desiccation degree (time×vapor pressure deficit) that the plants could tolerate under drought was reduced by 43-64% under elevated [CO2]. In addition, sensitivity analysis showed that P50 was the most important trait in determining the critical desiccation degree. Thus, our results demonstrated that whilst elevated [CO2] benefited plant growth under WS or SS, it also interfered with hydraulic acclimation, thereby potentially placing the plants at a higher risk of hydraulic failure and increased mortality.

Temperature dependence of spring carbon uptake in northern high latitudes during the past four decades.

In the northern high latitudes, warmer spring temperatures generally lead to earlier leaf onsets, higher vegetation production, and enhanced spring carbon uptake. Yet, whether this positive linkage has diminished under climate change remains debated. Here, we used atmospheric CO2 measurements at Barrow (Alaska) during 1979-2020 to investigate the strength of temperature dependence of spring carbon uptake reflected by two indicators, spring zero-crossing date (SZC) and CO2 drawdown (SCC). We found a fall and rise in the interannual correlation of temperature with SZC and SCC (RSZC-T and RSCC-T ), showing a recent reversal of the previously reported weakening trend of RSZC-T and RSCC-T . We used a terrestrial biosphere model coupled with an atmospheric transport model to reproduce this fall and rise phenomenon and conducted factorial simulations to explore its potential causes. We found that a strong-weak-strong spatial synchrony of spring temperature anomalies per se has contributed to the fall and rise trend in RSZC-T and RSCC-T , despite an overall unbroken temperature control on net ecosystem CO2 fluxes at local scale. Our results provide an alternative explanation for the apparent drop of RSZC-T and RSCC-T during the late 1990s and 2000s, and suggest a continued positive linkage between spring carbon uptake and temperature during the past four decades. We thus caution the interpretation of apparent climate sensitivities of carbon cycle retrieved from spatially aggregated signals.

Amphistomy: stomata patterning inferred from 13C content and leaf-side-specific deposition of epicuticular wax.

BACKGROUND AND AIMS: The benefits and costs of amphistomy (AS) vs. hypostomy (HS) are not fully understood. Here, we quantify benefits of access of CO2 through stomata on the upper (adaxial) leaf surface, using 13C abundance in the adaxial and abaxial epicuticular wax. Additionally, a relationship between the distribution of stomata and epicuticular wax on the opposite leaf sides is studied. METHODS: We suggest that the 13C content of long-chain aliphatic compounds of cuticular wax records the leaf internal CO2 concentration in chloroplasts adjacent to the adaxial and abaxial epidermes. This unique property stems from: (1) wax synthesis being located exclusively in epidermal cells; and (2) ongoing wax renewal over the whole leaf lifespan. Compound-specific and bulk wax 13C abundance (δ) was related to amphistomy level (ASL; as a fraction of adaxial in all stomata) of four AS and five HS species grown under various levels of irradiance. The isotopic polarity of epicuticular wax, i.e. the difference in abaxial and adaxial δ (δab - δad), was used to calculate the leaf dorsiventral CO2 gradient. Leaf-side-specific epicuticular wax deposition (amphiwaxy level) was estimated and related to ASL. KEY RESULTS: In HS species, the CO2 concentration in the adaxial epidermis was lower than in the abaxial one, independently of light conditions. In AS leaves grown in high-light and low-light conditions, the isotopic polarity and CO2 gradient varied in parallel with ASL. The AS leaves grown in high-light conditions increased ASL compared with low light, and δab - δad approached near-zero values. Changes in ASL occurred concomitantly with changes in amphiwaxy level. CONCLUSIONS: Leaf wax isotopic polarity is a newly identified leaf trait, distinguishing between hypo- and amphistomatous species and indicating that increased ASL in sun-exposed AS leaves reduces the CO2 gradient across the leaf mesophyll. Stomata and epicuticular wax deposition follow similar leaf-side patterning.

Influence of Human Activity on Radon Concentration, Indoor Air Quality, and Thermal Comfort in Small Office Spaces.

This article focuses on the influence of occupants on the concentration of contaminants (radon Rn-222 and CO2) as well as the thermal comfort parameters. A series of sensors were placed to measure the concentration of the contaminants, temperature, and relative humidity in the test room at the Institute of Nuclear Physics PAN in Krakow (IFJ PAN), Poland. The test room is an office that is typical of the offices used in the facility. The occupants that used the space kept a detailed diary of their entry, exit, and number of people entering. The results showed that the accumulation of contaminants in such spaces may be severe and risks the health and safety of occupants. The accumulation of CO2 was extremely noticeable and did not diminish to the background level between the re-entry of the occupants to the office. The same was consistent for the radon concentration. The study shows how ventilation methods and small changes in occupant work strategy may influence the contaminant concentration within a test zone.

Effects of CO2 concentration and time on algal biomass film, NO3-N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II.

The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3-N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3-N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3-N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3-N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3-N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3-N content.

Assessing the CO2 concentration at the surface of photosynthetic mesophyll cells.

We present a robust estimation of the CO2 concentration at the surface of photosynthetic mesophyll cells (cw ), applicable under reasonable assumptions of assimilation distribution within the leaf. We used Capsicum annuum, Helianthus annuus and Gossypium hirsutumas model plants for our experiments. We introduce calculations to estimate cw using independent adaxial and abaxial gas exchange measurements, and accounting for the mesophyll airspace resistances. The cw was lower than adaxial and abaxial estimated intercellular CO2 concentrations (ci ). Differences between cw and the ci of each surface were usually larger than 10 µmol mol-1 . Differences between adaxial and abaxial ci ranged from a few µmol mol-1 to almost 50 µmol mol-1 , where the largest differences were found at high air saturation deficits (ASD). Differences between adaxial and abaxial ci and the ci estimated by mixing both fluxes ranged from -30 to +20 µmol mol-1 , where the largest differences were found under high ASD or high ambient CO2 concentrations. Accounting for cw improves the information that can be extracted from gas exchange experiments, allowing a more detailed description of the CO2 and water vapor gradients within the leaf.

18 O enrichment of sucrose and photosynthetic and nonphotosynthetic leaf water in a C3 grass-atmospheric drivers and physiological relations.

The 18 O enrichment (Δ18 O) of leaf water affects the Δ18 O of photosynthetic products such as sucrose, generating an isotopic archive of plant function and past climate. However, uncertainty remains as to whether leaf water compartmentation between photosynthetic and nonphotosynthetic tissue affects the relationship between Δ18 O of bulk leaf water (Δ18 OLW ) and leaf sucrose (Δ18 OSucrose ). We grew Lolium perenne (a C3 grass) in mesocosm-scale, replicated experiments with daytime relative humidity (50% or 75%) and CO2 level (200, 400 or 800 µmol mol-1 ) as factors, and determined Δ18 OLW , Δ18 OSucrose and morphophysiological leaf parameters, including transpiration (Eleaf ), stomatal conductance (gs ) and mesophyll conductance to CO2 (gm ). The Δ18 O of photosynthetic medium water (Δ18 OSSW ) was estimated from Δ18 OSucrose and the equilibrium fractionation between water and carbonyl groups (εbio ). Δ18 OSSW was well predicted by theoretical estimates of leaf water at the evaporative site (Δ18 Oe ) with adjustments that correlated with gas exchange parameters (gs or total conductance to CO2 ). Isotopic mass balance and published work indicated that nonphotosynthetic tissue water was a large fraction (~0.53) of bulk leaf water. Δ18 OLW was a poor proxy for Δ18 OSucrose , mainly due to opposite Δ18 O responses of nonphotosynthetic tissue water (Δ18 Onon-SSW ) relative to Δ18 OSSW , driven by atmospheric conditions.

SARS-CoV-2 incidence in secondary schools; the role of national and school-initiated COVID-19 measures.

INTRODUCTION: Our aim was to gain insight into the effect of COVID-19 measures on SARS-CoV-2 incidence in secondary schools and the association with classroom CO2 concentration and airborne contamination. METHODS: Between October 2020-June 2021, 18 schools weekly reported SARS-CoV-2 incidence and completed surveys on school-initiated COVID-19 measures (e.g. improving hygiene or minimizing contacts). CO2 was measured in occupied classrooms twice, and SARS-CoV-2 air contamination longitudinally using electrostatic dust collectors (EDC) and analyzed using RT-qPCR. National COVID-19 policy measures varied during pre-lockdown, lockdown and post-lockdown periods. During the entire study, schools were recommended to improve ventilation. SARS-CoV-2 incidence rate ratios (IRR) were estimated by Generalized Estimating Equation (GEE) models. RESULTS: During 18 weeks follow-up (range: 10-22) SARS-CoV-2 school-incidence decreased during national lockdown (adjusted IRR: 0.41, 95%CI: 0.21-0.80) and post-lockdown (IRR: 0.60, 0.39-0.93) compared to pre-lockdown. School-initiated COVID-19 measures had no additional effect. Pre-lockdown, IRRs per 10% increase in time CO2 exceeded 400, 550 and 800 ppm above outdoor level respectively, were 1.08 (1.00-1.16), 1.10 (1.02-1.19), and 1.08 (0.95-1.22). Post-lockdown, CO2-concentrations were considerably lower and not associated with SARS-CoV-2 incidence. No SARS-CoV-2 RNA was detected in any of the EDC samples. CONCLUSION: During a period with low SARS-CoV-2 population immunity and increased attention to ventilation, with CO2 levels most of the time below acceptable thresholds, only the national policy during and post-lockdown of reduced class-occupancy, stringent quarantine, and contact testing reduced SARS-CoV-2 incidence in Dutch secondary schools. Widespread SARS-CoV-2 air contamination could not be demonstrated in schools under the prevailing conditions during the study.

Metabolomics analysis reveal the molecular responses of high CO2 concentration improve resistance to Pb stress of Oryza sativa L. seedlings.

Rice seedlings were exposed to two CO2 concentrations (400 ± 20 and 800 ± 20 µmol mol-1) and three PbNO3 concentrations (0, 50 and 100 µmol L-1) for 10 days to explore the regulatory mechanisms of elevated CO2 for Pb stress resistance. Electrical conductivity, MDA content, SOD, POD, CAT activities and metabolomics changes were studied. Results showed that: Pb stress damaged cell membrane system, electrical conductivity and MDA content increased 49.34 % and 73.27 %, respectively, and some antioxidant enzymes activities increased. Sugar, polyol, amino acid metabolism and fatty acid ß-oxidation were all enhanced to improve osmotic adjustments, maintain cell membrane stability, supply energy, nitrogen assimilates and antioxidant capacity; Under composite treatments, cell membrane damage was reduced, activities of protective enzymes increased compared with only Pb stress, POD activity increased the most (49.14 %) under severe Pb composite treatment. High CO2 caused the enhance of cells antioxidant capacity, TCA cycle intermediate products contents and fatty acid desaturation under mild Pb stress. Many sugars, polyols and amino acids contents were increased as osmotic regulatory substances by high CO2 under severe Pb stress; Secondary metabolites played an important role under Pb stress and composite treatments. The object of this study is to provide a possible molecular mechanism of rice response to Pb stress under high CO2 in the future.

A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking CO2 Concentration as an Example.

In a layer house, the CO2 (carbon dioxide) concentration above the upper limit can cause the oxygen concentration to be below the lower limit suitable for poultry. This leads to chronic CO2 poisoning in layers, which manifests as listlessness, reduced appetite, weak constitution, decreased production performance, and weakened resistance to disease. Regulating ventilation may ensure a suitable CO2 concentration in layer houses. Predicting the changes in CO2 concentration and regulating the CO2 concentration in advance are key to ensuring healthy large-scale breeding of layers. In recent years, machine learning and deep learning methods have been increasingly applied to this field. A CO2 prediction model for layer house is proposed based on a GRU (gated recurrent unit) and LSTM (long short-term memory). The temperature, humidity, and CO2 were determined as inputs to the model by the correlation coefficient. The datasets of the experimental layer house were continuously measured during June-July 2023, using a self-developed environmental monitor, and the monitored data were used as samples for model inputs. There were 22,000 time series data in the datasets. In this study, multivariate time series data were standardized via data pre-processing to improve model training. GRU and LSTM models were constructed. The models were trained using a training set. Then, these trained models were used to provide predictions on a test set. The prediction errors were calculated using the true values of the test set and the predicted values provided by the models. To test the performance of the model and accuracy of the predictions, predictions were made for different numbers of datasets. The results demonstrated that the combined prediction model had good generalization, stability, and convergence with high prediction accuracy. Due to the structure of the model, the stability of the LSTM model was higher than that of the GRU model, and its prediction accuracy and speed were lower than those of the GRU model. When the datasets of the GRU model were 15,000 to 17,000, The MAE of the GRU was 70.8077 to 126.7029 ppm, and the prediction time of the GRU is 16 to 24 ms. When the LSTM model's datasets were 15,000-20,000, the MAE of LSTM was 78.8596 to 136.0896 ppm, and the prediction time of the GRU was 17 to 26 ms.

Changes in the Level of DNA Methylation in Candida albicans under the Influence of Physical and Chemical Factors.

The effects of physical factors such as radiation (electromagnetic, microwave, infrared, laser, UVC, and X-ray) and high temperature, as well as chemical factors (controlled atmosphere) on the level of global DNA cytosine methylation in C. albicans ATCC 10231 cells were investigated. Prolonged exposure to each type of radiation significantly increased the DNA methylation level. In addition, the global methylation level in C. albicans cells increased with the incubation temperature. An increase in the percentage of methylated DNA was also noted in C. albicans cells cultured in an atmosphere with reduced O2. In contrast, in an atmosphere containing more than 3% CO2 and in anaerobic conditions, the DNA methylation level decreased relative to the control. This study showed that prolonged exposure to various types of radiation and high temperature as well as reduced O2 in the atmosphere caused a significant increase in the global DNA methylation level. This is most likely a response protecting DNA against damage, which at the same time can lead to epigenetic disorders, and in consequence can adversely affect the functioning of the organism.

A comprehensive meta-analysis of the impacts of intensified drought and elevated CO2 on forage growth.

Forage crops are used worldwide as key feed sources for dairy systems. However, their productivity and quality are limited due to intensified drought events, elevated carbon dioxide (CO2), and their interaction with climate change, with consequences for the security of animal husbandry and the agricultural economy. Although studies have quantified the impacts of such stresses on forage growth, these impacts have been less systematically investigated in a global context due to differences among various forage groups, regional microclimates, and environmental factors. Herein we employed nine forage growth-related variables involving three perspectives, i.e., photosynthetic parameters, production, and quality, from research articles published between 1990 and 2021 via a meta-analysis. A linear mixed-effect model was then used to explore the quantitative relationship between these factors in a restricted dataset. Decreasing trends in all four photosynthetic parameters were detected across different eco-geographical regions with increasing drought stress. The maximum decrease in DMY occurred in the Mediterranean, with 52.8% under drought conditions. Globally, eCO2 significantly increased photosynthetic rate (Pn) and instantaneous water use efficiency (WUEi) by 40.8% and 62.1%, respectively, which also had positive effects on forage dry matter yield (DMY) (+25.1%), especially for forage in Northern Europe. However, this stress would significantly decrease forage quality by decreasing crude protein (CP) (-19.7%) and nitrogen content (N content) (-13.5%). These negative impacts would be aggravated under the co-occurrence of drought and eCO2, including a significant increase in WUEi (+111.1%) and a decrease in DMY (-12.3%). Gramineae showed a more sensitive response to drought stress in photosynthetic parameters and DMY than Leguminosae, but the latter exhibited a better response in photosynthetic parameters and production under eCO2. Our analysis provides a consensus concerning how the growth parameters of forage have changed under environmental stresses.

Salinity, waterlogging, and elevated [CO2] interact to induce complex responses in cultivated and wild tomato.

The effects of individual climatic factors on crops are well documented, whereas the interaction of such factors in combination has received less attention. The frequency of salinity and waterlogging stress is increasing with climate change, accompanied by elevated CO2 concentration (e[CO2]). This study explored how these three variables interacted and affected two tomato genotypes. Cultivated and wild tomato (Solanum lycopersicum and Solanum pimpinellifolium) were grown at ambient [CO2] and e[CO2], and subjected to salinity, waterlogging, and combined stress. Leaf photosynthesis, chlorophyll fluorescence, quenching analysis, pigment, and plant growth were analyzed. The response of tomatoes depended on both genotype and stress type. In cultivated tomato, photosynthesis was inhibited by salinity and combined stress, whereas in wild tomato, both salinity and waterlogging stress, alone and in combination, decreased photosynthesis. e[CO2] increased photosynthesis and biomass of cultivated tomato under salinity and combined stress compared with ambient [CO2]. Differences between tomato genotypes in response to individual and combined stress were observed in key photosynthetic and growth parameters. Hierarchical clustering and principal component analysis revealed genetic variations of tomatoes responding to the three climatic factors. Understanding the interacting effects of salinity and waterlogging with e[CO2] in tomato will facilitate improvement of crop resilience to climate change.

Investigation of Indoor Air Quality in Residential Buildings by Measuring CO2 Concentration and a Questionnaire Survey.

Indoor air quality (IAQ) in houses is often deteriorated by chemical substances emitted from heating, building materials, or other household goods. Since it is difficult for occupants to recognize air pollution, they rarely understand the actual conditions of the IAQ. An investigation into the actual condition of IAQ in houses was therefore conducted in this study. Carbon dioxide (CO2) concentrations in 24 occupied houses was measured, and the results from our analysis showed that the use of combustion heaters increased the concentration of CO2 and led to indoor air pollution. Results indicate that as outdoor temperature decreased, the frequency of ventilation decreased simultaneously, and CO2 concentration increased. Results of the questionnaire survey revealed that the actual IAQ in each house did not match the level of awareness its occupants had regarding ventilation. Along with this difficulty in perceiving air pollution, the lack of knowledge about ventilation systems and the effects of combustion heating may be additional barriers to IAQ awareness.

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building.

The measurement of the CO2 concentration has a wide range of applications. Traditionally, it has been used to assess air quality, with other applications linked to the experimental assessment of occupancy patterns and air renewal rates. More recently, the worldwide dissemination of COVID-19 establishing a relationship between infection risk and the mean CO2 level has abruptly led to the measurement of the CO2 concentration in order to limit the spread of this respiratory disease in the indoor environment. Therefore, the extensive application of this measurement outside of traditional air quality assessment requires an in-depth analysis of the suitability of these sensors for such modern applications. This paper discusses the performance of an array of commercial wall-mounted CO2 sensors, focusing on their application to obtain occupancy patterns and air renovation rates. This study is supported by several long-term test campaigns conducted in an in-use office building located in south-eastern Spain. The results show a spread of 19-101 ppm, with a drift of 28 ppm over 5 years, an offset of 2-301 ppm and fluctuations up to 80 ppm in instantaneous measurements not related to concentration changes. It is proposed that values averaged over 30 min, using a suitable reference value, be used to avoid erroneous results when calibration is not feasible.

Evolution and Neural Network Prediction of CO2 Emissions in Weaned Piglet Farms.

This paper aims to study the evolution of CO2 concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weight based on setpoint temperature, outdoor temperature, and ventilation flow. The experimental trial was conducted during one transition cycle. Generally, the ventilation flow increased with the reduction in setpoint temperature throughout the cycle, which caused a reduction in CO2 concentration and an increase in emissions. The mean CO2 concentration was 3.12 g m-3. Emissions of CO2 had a mean value of 2.21 mg s-1 per animal, which is equivalent to 0.195 mg s-1 kg-1. A potential function was used to describe the interaction between 10 min values of ventilation flow and CO2 concentrations, whereas a linear function was used to describe the interaction between 10 min values of ventilation flow and CO2 emissions, with r values of 0.82 and 0.85, respectively. Using such equations allowed for simple and direct quantification of emissions. Furthermore, two prediction models for CO2 emissions were developed using two neural networks (for 10 min and 60 min predictions), which reached r values of 0.63 and 0.56. These results are limited mainly by the size of the training period, as well as by the differences between the behavior of the series in the training stage and the testing stage.