Relative Vapor Pressure Research Articles

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

Development of anti-corrosion coating with sandwich-like microvascular network for realization of self-healing and self-reporting properties based on coaxial electrospinning

Biomimetic microvascular networks prepared by coaxial electrospinning technology have attracted much attention as a novel form of carrier for encapsulating active substances. However, the poor interfacial bonding strength between traditional electrospinning materials and metal substrates limits its application in anti-corrosion coatings. Herein, a novel poly(vinyl alcohol) grafted phytic acid (PVA-PA) electrospinning solution was synthesized. And a sandwich-like microvascular network (SMN) was prepared by coaxial electrospinning technology, which used PVA-PA solution as the shell material, epoxy resin 51 (E51), tetraphenylethylene (TPE) and polyamide resin as the core materials, respectively. Owing to the high porosity of SMN, epoxy resin can be directly spin-coated on it to form a composite coating (PVA-PA/SMN/EP). It is proved that due to the strong chelation and coordination interaction between PA and mild steel, the pull-out adhesion of the PVA-PA/SMN/EP composite coatings on mild steel was increased by 0.92 MPa. In addition, by systematically optimizing the relative viscosity, miscibility, conductivity, and saturated vapor pressure between the two jets of the core solution and the shell solution in coaxial electrospinning, the microvascular structure of the coaxial electrospinning nanofibers was improved and the fluidity of the internal active substances was maintained. When the PVA-PA/SMN/EP composite coating generates microcracks, the active substances encapsulated in the SMN flow out, in which the three-dimensional crosslinked network formed by the curing of E51 and polyamide resin enhances the spatial interactions between TPE molecules, which results in TPE emitting a bright blue fluorescence. This work provides a new approach for the development of the next generation of smart anti-corrosion coatings.

Responses of sap flow in Larix principis-rupprechtii to environment under different weather conditions.

This study was conducted in Liupanshan Forest Ecological Positioning Station of the National Forestry and Grassland Administration. We monitored sap flow of Larix principis-rupprechtii plantation in the Xiangshui River sub-basin throughout the 2019 growing season (from May 17 to October 12), as well as the meteorological conditions and soil environment (soil temperature and soil water content), to analyze the comprehensive environmental responses of sap flow in L. principis-rupprechtii under different weather conditions. The results showed that sap flow rate increased and then decreased on the daily scale, with the highest rate on sunny days, followed by overcast days and then rainy days. Sap flow rate had a single peak on sunny days and multiple peaks on overcast and rainy days. Sunny days had earlier and longer sap flow compared to overcast and rainy days. Dominant factors driving sap flow differed across different weather. Vapor pressure deficit was the dominant factor influencing sap flow in sunny and overcast days, while solar radiation was dominant one in rainy days. The contribution rates of main factors to sap flow on sunny, overcast and rainy days were 31.1%, 27.4% and 40.1%, respectively. Results of the principal component analysis showed the factors affecting sap flow on sunny days could be classified into hydrothermal complex factors (air temperature, soil temperature, and volumetric soil moisture), water vapor transpiration factors (relative humidity and vapor pressure deficit), and radiation factor (solar radiation). The factors affecting sap flow on overcast and rainy days were combined into transpiration (relative humidity, solar radiation, and vapor pressure deficit), heat (air temperature and soil temperature), and soil water factor volumetric (volumetric soil moisture). On sunny days, sap flow reached the peak value 110, 80, 70 min after the hydrothermal, water vapor transpiration, and radiation factors, respectively. On overcast and rainy days, sap flow reached its peak in 10, 20, 30 min and 140, 60, 150 min, respectively before the peaks of transpiration, heat, and soil water factors.

Simulating atmospheric drought: Silica gel packets dehumidify mesocosm microclimates.

As global temperatures rise, droughts are becoming more frequent and severe. To predict how drought might affect plant communities, ecologists have traditionally designed drought experiments with controlled watering regimes and rainout shelters. Both treatments have proven effective for simulating soil drought. However, neither are designed to directly modify atmospheric drought. Here, we detail the efficacy of a silica gel atmospheric drought treatment in outdoor mesocosms with and without a co-occurring soil drought treatment. At California State University, Los Angeles, we monitored relative humidity, temperature, and vapor pressure deficit every 10 min for 5 months in bare-ground, open-top mesocosms treated with soil drought (reduced watering) and/or atmospheric drought (silica dehumidification packets suspended 12 cm above soil). We found that silica packets dehumidified these mesocosm microclimates most effectively (-5% RH) when combined with reduced soil water, regardless of the ambient humidity levels of the surrounding air. Further, packets increased microclimate vapor pressure deficit most effectively (+0.4 kPa) when combined with reduced soil water and ambient air temperatures above 20°C. Finally, packets simulated atmospheric drought most consistently when replaced within 3 days of deployment. Our results demonstrate the use of silica packets as effective dehumidification agents in outdoor drought experiments. We emphasize that incorporating atmospheric drought in existing soil drought experiments can improve our understandings of the ecological impacts of drought.

Evaluation of potential increase in photosynthetic efficiency of cassava (Manihot esculenta Crantz) plants exposed to elevated carbon dioxide.

Cassava (Manihot esculenta Crantz), an important tropical crop, is affected by extreme climatic events, including rising CO2 levels. We evaluated the short-term effect of elevated CO2 concentration (ECO2 ) (600, 800 and 1000ppm) on the photosynthetic efficiency of 14 cassava genotypes. ECO2 significantly altered gaseous exchange parameters (net photosynthetic rate (P n ), stomatal conductance (g s ), intercellular CO2 (C i ) and transpiration (E )) in cassava leaves. There were significant but varying interactive effects between ECO2 and varieties on these physiological characteristics. ECO2 at 600 and 800ppm increased the P n rate in the range of 13-24% in comparison to 400ppm (ambient CO2 ), followed by acclimation at the highest concentration of 1000ppm. A similar trend was observed in g s and E . Conversely, C i increased significantly and linearly across increasing CO2 concentration. Along with C i , a steady increase in water use efficiency [WUEintrinsic (P n /g s ) and WUEinstantaneous (P n /E )] across various CO2 concentrations corresponded with the central role of restricted stomatal activity, a common response under ECO2 . Furthermore, P n had a significant quadratic relationship with the ECO2 (R 2 =0.489) and a significant and linear relationship with C i (R 2 =0.227). Relative humidity and vapour pressure deficit during the time of measurements remained at 70-85% and ~0.9-1.31kPa, respectively, at 26±2°C leaf temperature. Notably, not a single variety exhibited constant performance for any of the parameters across CO2 concentrations. Our results indicate that the potential photosynthesis can be increased up to 800ppm cassava varieties with high sink capacity can be cultivated under protected cultivation to attain higher productivity.

The Variation in Atmospheric Turbidity over a Tropical Site in Nigeria and Its Relation to Climate Drivers

Atmospheric turbidity exhibits substantial spatial–temporal variability due to factors such as aerosol emissions, seasonal changes, meteorology, and air mass transport. Investigating atmospheric turbidity is crucial for climatology, meteorology, and atmospheric pollution. This study investigates the variation in atmospheric turbidity over a tropical location in Nigeria, utilizing the Ångström exponent (α), the turbidity coefficient (β), the Linke turbidity factor (TL), the Ångström turbidity coefficient (βEST), the Unsworth–Monteith turbidity coefficient (KAUM), and the Schüepp turbidity coefficient (SCH). These parameters were estimated from a six-month uninterrupted aerosol optical depth dataset (January–June 2016) and a one-year dataset (January–December 2016) of solar radiation and meteorological data. An inverse correlation (R = −0.77) was obtained between α and β, which indicates different turbidity regimes based on particle size. TL and βEST exhibit pronounced seasonality, with higher turbidity during the dry season (TL = 9.62 and βEST = 0.60) compared to the rainy season (TL = 0.48 and βEST = 0.20) from May to October. Backward trajectories and wind patterns reveal that high-turbidity months align with north-easterly air flows from the Sahara Desert, transporting dust aerosols, while low-turbidity months coincide with humid maritime air masses originating from the Gulf of Guinea. Meteorological drivers like relative humidity and water vapor pressure are linked to turbidity levels, with an inverse exponential relationship observed between normalized turbidity coefficients and normalized water vapor pressure. This analysis provides insights into how air mass origin, wind patterns, and local climate factors impact atmospheric haze, particle characteristics, and solar attenuation variability in a tropical location across seasons. The findings can contribute to environmental studies and assist in modelling interactions between climate, weather, and atmospheric optical properties in the region.

VPD-based models of dead fine fuel moisture provide best estimates in a global dataset

Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD). A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available from https://hcfm.shinyapps.io/shinyfmd/.

Atmospheric drying and soil drying: Differential effects on grass community composition

AbstractGlobal surface temperatures are projected to increase in the future; this will modify regional precipitation regimes and increase global atmospheric drying. Despite many drought studies examining the consequences of reduced precipitation, there are few experimental studies exploring plant responses to atmospheric drying via relative humidity and vapor pressure deficit (VPD). We examined eight native California perennial grass species grown in pots in a greenhouse in Los Angeles, California for 34 weeks. All pots were well‐watered for 21 weeks, at which point we reduced watering to zero and recorded daily growth and dormancy for 3 weeks. We used this information to better understand the drought tolerance of our species in a larger soil drying × atmospheric drying experiment. In this larger experiment, we grew all eight species together in outdoor mesocosms and measured changes in community composition after 4 years of growth. Soil drying in our small pot experiment mirrored compositional shifts in the larger experiment. Namely, our most drought‐tolerant species in our pot experiment was Poa secunda, due to a summer dormancy strategy. Similarly, the grass community shifted toward P. secunda in the driest soils as P. secunda was mostly unaffected by either soil drying or atmospheric drying. We found that some species responded strongly to soil drying (Elymus glaucus, Festuca idahoensis, and Hordeum b. californicum), while others responded strongly to atmospheric drying (Bromus carinatus and Stipa cernua). As result, community composition shifted in different and interacting ways in response to soil drying, atmospheric drying, and their combination. Further study of community responses to increasing atmospheric aridity is an essential next step to predicting the future consequences of climate change.

The microstructure characteristics of the 1-ethyl-3-methylimidazolium thiocyanate–methyl acetate−methanol systems

The ionic liquid (IL) has demonstrated promising potential as an entrainer in efficiently separating the methyl acetate−methanol azeotropic mixture using extractive distillation. However, comprehending the fundamental mechanism responsible for azeotropism vanished by an IL entrainer is crucial for its large-scale applications. This study aims to explore the microstructure characteristics of the 1-ethyl-3-metfighylimidazolium thiocyanate ([EMIM][SCN])–methyl acetate−methanol systems using FTIR and DFT calculations. The focus is on understanding the fundamental reasons behind azeotrope breaking facilitated by the IL entrainer. Initially, the v(aromatic C–H) region of the imidazolium cation was analyzed to evaluate the interaction strength between the entrainer and methyl acetate/methanol. The results indicated stronger interactions between [EMIM][SCN] and methanol. As a consequence of this variation in interaction strength, the relative vapor pressure of the two constituents was impacted, ultimately resulting in the disturbance of the azeotrope. Subsequently, the v(C=O) region of methyl acetate was utilized to detect interaction complexes in various systems, assisted by excess spectra. Various interaction species, including methyl acetate–methanol/2methanol and methyl acetate–ion cluster/ion pair/cation were found. Notably, when the mole fraction of IL exceeded 0.087, the IL effectively disrupted the interaction complex between methyl acetate and methanol, thereby eliminating azeotropy in the methyl acetate–methanol system.

Long-term growth and xylem hydraulic responses of Albizia procera (Roxb.) Benth. to climate in a moist tropical forest of Bangladesh

Climate change is a serious concern around the world, particularly in tropical regions including Bangladesh. Yet, how tree growth and hydraulic behavior of Bangladeshi native tree species changed in response to past climate variability and changes have not been adequately understood. We developed the first ring-width and vessel chronologies of Albizia procera (Roxb.) Benth. from a moist tropical forest of Bangladesh (Rema-Kalenga Wildlife Sanctury, RKWS) to analyze the impact of inter-annual climate variability on tree growth and xylem hydraulic traits. The chronologies contained common environmental signals as shown by the values of expressed population signal (EPS) and other statistical parameters. Climate-growth analysis showed that maximum temperature (Tmax) favored tree growth at the end of the wet season (November). Among the vessel and hydraulic trait chronologies, number of vessels (NV) had significant positive relation with May minimum temperature (Tmin) and vessel density (VD) had a negative relationship with April Tmin. Precipitation had a negative relation with vessel density (VD), and the potential specific hydraulic conductivity (KS). Relative humidity (RH) and vapour pressure deficit (VPD) had contrasting effects on vessel and hydraulic traits. On a regional scale, the ring-width index and vessel chronologies were correlated with both gridded land surface temperature and precipitation, but during different periods of the year. Linear mixed effect modeling revealed significant positive relationships between VD and Tmax implying a good acclimation potential of this tree to rising temperature. However, the absence of the generally expected trade-off between VD and DH calls for further studies on the hydraulic functions of this species in moist tropical forests.

Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (°C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram.

Surface Condensation Water under Salix psammophila Is the Main Water Source in Addition to Rainfall in the Kubuqi Desert

Amidst climate change, managing water and vegetation adaptability is vital in ecology and agriculture. Salix psammophila is key in deserts, maintaining ecological balance and combating desertification. Understanding its surface condensation and response to weather is critical for survival. This study aims to investigate the formation patterns of surface condensation water under S. psammophila at different irrigation levels and the influence of precipitation, temperature, relative humidity, and wind speed. Conducted in China’s Kubuqi Desert from June to September 2022, the study employed micro-lysimeters with four irrigation test sets and a control group, conducting detailed observations at various locations. The results indicate that S. psammophila significantly influences surface condensation water by blocking solar radiation and reducing wind speed. The drip irrigation system also regulates surface condensation water on S. psammophila. Moreover, meteorological factors such as 24 h maximum temperature, relative humidity difference, wind speed, and air vapor pressure deficit show significant correlations with surface condensation water formation. In conclusion, this study enhances the understanding of desert ecosystem water balance, vegetation adaptability, and efficient water resource utilization. It provides valuable scientific guidance for the conservation and restoration of desert ecosystems.

Urban forest microclimates across temperate Europe are shaped by deep edge effects and forest structure

The urban heat island (UHI) causes strong warming of cities and their urban forests worldwide. Especially urban forest edges are strongly exposed to the UHI effect, which could impact urban forest biodiversity and functioning. However, it is not known to what extent the UHI effect alters edge-to-interior microclimatic gradients within urban forests and whether this depends on the forests’ structure.Here we quantified gradients of air temperature, relative air humidity and vapour pressure deficits (VPD) along urban forest edge-to-interior transects with contrasting stand structures in six major cities across Europe. We performed continuous hourly microclimate measurements for two consecutive years and analysed the magnitude and depth of edge effects, as well as forest structural drivers of microclimatic variation.Compared to edge studies in rural temperate forests, we found that edge effects reached deeper into urban forests, at least up to 50 m. Throughout the year, urban forest edges were warmer and drier compared to forest interiors, with the largest differences occurring during summer and daytime. Not only maximum, but also mean and minimum temperatures were higher at the urban forest edge up to large edge distances (at least 85 m). Denser forests with a higher plant area index buffered high air temperatures and VPDs from spring to autumn.We conclude that urban forest edges are unique ecotones with specific microclimates shaped by the UHI effect. Both forest edges and interiors showed increased buffering capacities with higher forest canopy density. We advocate for the conservation and expansion of urban forests which can buffer increasingly frequent and intense climate extremes. To this end, urban forest managers are encouraged to aim for multi-layered dense forest canopies and consider edge buffer zones of at least 50 m wide.

The mechanism of the azeotropy eliminating in dimethyl carbonate–ethanol system by extractive distillation applying ionic liquid [BMIM][Tf2N] as entrainer: A combination of FTIR and DFT study

Owing to their outstanding properties, ionic liquids (ILs) have been increasingly applied as entrainers to separate alcohol azeotrope systems in extractive distillation. Fundamental studies on the mechanism of azeotrope breaking are essential for the better use of ILs; however, they are not often reported. To reveal the intrinsic cause of azeotrope breaking by an IL entrainer, on this work, the microstructure features of IL–azeotrope systems were investigated using FTIR and DFT calculations. 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([BMIM][Tf2N]) and the azeotrope systems dimethyl carbonate (DMC) and ethanol were selected. The v(aromatic C–H) region of the imidazolium cation was analysed first to distinguish the relative interaction strength between the IL and ethanol/DMC. The v(O–D) region of ethanol was applied to identify the interaction complexes in different mixtures and estimate the relative interaction strength between ethanol and the IL/DMC. Combined with DFT calculations, it was found that interactions between [BMIM][Tf2N] and ethanol were stronger than those between [BMIM][Tf2N] and DMC, which could influence the relative vapour pressure of the two constituents, thus breaking the azeotrope. The interactions between ethanol and the IL were stronger than those between ethanol and DMC. Thus, the addition of the IL could break the interaction complex of DMC–ethanol, which is the cause of azeotropy in the DMC–ethanol system. When x(IL) was larger than 0.170, the entire DMC–ethanol complex disappeared accompanied with the eliminated of the azeotrope.

Weather parameters as a predictive tool potentially allowing for better monitoring of dairy cattle against gastrointestinal parasites hazard

In animal production, yield is critically related to animal health status. To ensure high productivity, innovative control strategies for herd and parasites monitoring are required. Gastrointestinal parasites have a strong influence on changing feed intake or nutrient use, limiting animal productivity. Serological control has been proposed, given that parasite development is largely dependent on environmental temperature and humidity. However, breeders and field veterinarians lack readily accessible climate characteristics that provide information to determine whether and when herds require laboratory examination. To help reduce the testing costs incurred by farmers, we investigated whether selected meteorological data could serve as conclusive predictors to increase the precision of herd selection for serological monitoring. Our results indicate that the selection of herds by farmers for testing can be guided by regular checking of meteorological data, especially various temperature and humidity indicators. In general, ranges of 24–28 °C, as well as − 0.5 to 7.5 °C for the monthly maximum and minimum temperature, respectively, and relative humidity (68–79%) and vapour pressure (10–15 hPa) correspond to a high antiparasitic response of the herd, expressed as the optical density ratio. It is recommended to introduce coproscopic and/or serological tests if the observed weather pattern (covering the prepatent period of parasite development) ranges within the estimated values.

Net Primary Productivity Estimation Using a Modified MOD17A3 Model in the Three-River Headwaters Region

Remote sensing (RS) models can easily estimate the net primary productivity (NPP) on a large scale. The majority of RS models try to couple the effects of temperature, water, stand age, and CO2 concentration to attenuate the maximum light use efficiency (LUE) in the NPP models. The water effect is considered the most unpredictable, significant, and challenging. Because the stomata of alpine plants are less sensitive to limiting water vapor loss, the typically employed atmospheric moisture deficit or canopy water content may be less sensitive in signaling water stress on plant photosynthesis. This study introduces a soil moisture (SM) content index and an alpine vegetation photosynthesis model (AVPM) to quantify the RS NPP for the alpine ecosystem over the Three-River Headwaters (TRH) region. The SM content index was based on the minimum relative humidity and maximum vapor pressure deficit during the noon, and the AVPM model was based on the framework of a moderate resolution imaging spectroradiometer NPP (MOD17) model. A case study was conducted in the TRH region, covering an area of approximately 36.3 × 104 km2. The results demonstrated that the AVPM NPP greatly outperformed the MOD17 and had superior accuracy. Compared with the MOD17, the average bias of the AVPM was −9.8 gCm−2yr−1, which was reduced by 91.8%. The average mean absolute percent error was 57.0%, which was reduced by 68.2%. The average Pearson’s correlation coefficient was 0.4809, which was improved by 30.0%. The improvements in the NPP estimation were mainly attributed to the decreasing estimation of the water stress coefficient on the NPP, which was considered the higher constraint of water impact on plant photosynthesis. Therefore, the AVPM model is more accurate in estimating the NPP for the alpine ecosystem. This is of great significance for accurately assessing the vegetation growth of alpine ecosystems across the entire Qinghai–Tibet Plateau in the context of grassland degradation and black soil beach management.

Critical analysis of the potential of Psidium guajava cv Paluma (guava tree) for ozone biomonitoring under seasonal subtropical climate

• Psidium guajava is an ozone subtropical bioindicator. • Air temperature and relative humidity are important variables for ozone bioindication. • Multivariate analysis is a useful tool for biomonitoring in marked seasonality environments. Psidium guajava cv. Paluma (guava) has been emphasized as good indicator of phytotoxic tropospheric O 3 levels in studies conducted under both controlled and field conditions. However, its performance as bioindicator was never critically evaluated under subtropical climate featured by well-defined wet and dry seasons. The current field study provided new contributions on this aspect. Guava cv. Paluma plants were exposed to ozone in several locations of a metropolitan region in SE Brazil - which is featured by well-defined climate seasonality - to collect data to be used to describe seasonal and spatial variations in leaf injury index and in other leaf traits. Multilinear biomonitoring model was also adjusted for different O 3 pollution descriptors (daily O 3 concentration, AOT40, SUM00 and SUM60), meteorological conditions (air temperature and relative humidity, solar radiation, and vapor pressure deficit) and morphological leaf traits (visible leaf injury, leaf area, leaf dry mass, and leaf mass per leaf area). Visible leaf injuries tended to be higher in locations near the industrial pole. Autumn was the season when guava plants recorded the highest leaf injury caused by O 3 . Linear regression analysis did not indicate significant association between different O 3 descriptors and mean leaf injury; however, multilinear regression analysis has shown that other independent variables increased the bioindicator model’s explicability (R 2 = 0.67). Mean leaf injury observed for the 3rd, 4th and 5th oldest guava cv. Paluma leaves were predicted based on combined effects of air temperature and relative humidity, as well as on O 3 expressed as AOT40. The other leaf traits were excluded from the multilinear model. The adjusted model was validated as significant tool for future studies about air quality in subtropical regions with seasonal climate.

Investigation of the cellulose-water relationship by the pressure plate method

The water retention properties of papermaking fibers have been investigated by a "pressure plate" technique which permits a measurement of equilibrium moisture contents of pulp samples at precisely controlled relative vapor pressures between 0.99 and 1.00. This range encompasses the water contents of interest in papermaking from the table to the middle of the drier section. Water retention isotherms are drawn and are satisfactorily related to desorption isotherms which have been reported in the literature. Isotherms for a variety of pulps are reported. These are related to the papermaking properties of the fibers and show the effects of wood species, cooking method, pulp yield, bleaching, drying, and beating on the cellulose-water relationship.

Study of water vapor sorption by cellulose esters with different degrees of substitution

In this research, the sorption of water vapor (WV) by various cellulose esters with different degrees of substitution (SD) has been studied. It was found that if the sorption value is measured in moles of H2O per mole of repeating units of the esters, then sorption properties depend only on the degree of substitution and not on the type of substituent. The lowest sorption is observed for triesters due to the absence of hydroxyl groups, which have the greatest contribution to the sorption process. To describe sorption isotherms, a calculating equation A = Ao(1- K lnφ)-1 was derived, where Ao is the maximum sorption value at relative vapor pressure φ=1, and K is the coefficient. This equation allows adequately describing experimental S-shaped sorption isotherms of WV for various esters having different SD. It was shown that the most probable sorption mechanism is the binding of polar water molecules by polar groups of various esters.

Direct and Indirect Effects of Long-Term Field Warming Methods on the Physical Environment and Biological Responses in a Subtropical Forest

Tree growth may be affected by rising temperature. We conducted two long-term, independent warming experiments in a subtropical forest; one experiment used translocation warming and one experiment used infra-red (IR) warming. Both warming techniques are designed to increase air and soil temperatures (Tair and Tsoil), but may also differentially affect other environmental variables, including soil volumetric water content (SVWC), air relative humidity (RH) and vapor pressure deficit (VPD). Hence, tree response ascribed to Tair and Tsoil may be dependent on the indirect effects of the warming techniques. We experimentally tested these ideas on three native tree species (Machilus breviflora, Syzygium rehderianum, and Schima superba), which occurred at all experimental sites, in subtropical China. We translocated trees from higher elevation sites to lower elevation sites in the coniferous and broadleaf mixed forest (Tair was 0.68 ± 0.05°C higher; 8 years) and mountain evergreen broadleaf forest (Tair was 0.95 ± 0.06°C and 1.63 ± 0.08°C higher; 8 years). IR warming was imposed at an experimental site in a monsoon evergreen broadleaf forest (Tair was 1.82 ± 0.03°C higher; 5 years). We found that both methods directly increased Tair and Tsoil (although to varying degrees), while translocation warming indirectly dried the soil (lower SVWC) and IR warming indirectly dried the air (lower RH and higher VPD). Machilus breviflora exposed to translocation warming exhibited lower photosynthesis due to higher Tsoil and lower SVWC, leading to declining growth. Higher Tair and Tsoil due to translocation warming increased photosynthesis and growth for S. superba. Trees exposed to IR warming exhibited reduced photosynthesis due to lower RH (M. breviflora) and to lower stomatal conductance (gs) as a function of higher Tair (S. rehderianum and S. superba). This study highlights the potential direct and indirect effects of different warming techniques on the physical environment of forest ecosystems, and subsequently their impacts on biological traits of trees. Hence, different warming techniques may provide different outcomes when assessing the impact of warming on trees in future climates.