Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests

Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (R_ECO) in deciduous forests. Previous studies showed that empirical R_ECO models can be substantially improved by considering the biotic dependency of R_ECO on the short‐term productivity (e.g., daily gross primary production, GPP) in addition to the well‐known environmental controls of temperature and water availability. Here, we use a model‐data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling R_ECO at 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data‐oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled R_ECO when compared to a version of the model that does not consider the physiological phenology. The reduction of the model‐observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model‐error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling R_ECO in deciduous forests by including the phenological cycle of the canopy.     

Gross primary production is stimulated for three Populus species grown under free-air CO2 enrichment from planting through canopy closure

How forests will respond to rising [CO₂] in the long term is uncertain, most studies having involved juvenile trees in chambers prior to canopy closure. Poplar free‐air CO₂ enrichment (Viterbo, Italy) is one of the first experiments to grow a forest from planting through canopy closure to coppice, entirely under open‐air conditions using free‐air CO₂ enrichment technology. Three Populus species: P. alba, P. nigra and P. x euramericana, were grown in three blocks, each containing one control and one treatment plot in which CO₂ was elevated to the expected 2050 concentration of 550 ppm. The objective of this study was to estimate gross primary production (GPP) from recorded leaf photosynthetic properties, leaf area index (LAI) and meteorological conditions over the complete 3‐year rotation cycle. From the meteorological conditions recorded at 30 min intervals and biweekly measurements of LAI, the microclimate of leaves within the plots was estimated with a radiation transfer and energy balance model. This information was in turn used as input into a canopy microclimate model to determine light and temperature of different leaf classes at 30 min intervals which in turn was used with the steady‐state biochemical model of leaf photosynthesis to compute CO₂ uptake by the different leaf classes. The parameters of these models were derived from measurements made at regular intervals throughout the coppice cycle. The photosynthetic rates for different leaf classes were summed to obtain canopy photosynthesis, i.e. GPP. The model was run for each species in each plot, so that differences in GPP between species and treatments could be tested statistically. Significant stimulation of GPP driven by elevated [CO₂] occurred in all 3 years, and was greatest in the first year (223–251%), but markedly lower in the second (19–24%) and third years (5–19%). Increase in GPP in elevated relative to control plots was highest for P. nigra in 1999 and for P. x euramericana in 2000 and 2001, although in 1999 P. alba had a higher GPP than P. x euramericana. Our analysis attributed the decline in stimulation to canopy closure and not photosynthetic acclimation. Over the 3‐year rotation cycle from planting to harvest, the cumulative GPP was 4500, 4960 and 4010 g C m^?2 for P. alba, P. nigra and P. x euramericana, respectively, in current [CO₂] and 5260, 5800 and 5000 g C m^?2 in the elevated [CO₂] treatments. The relative changes were consistent with independent measurements of net primary production, determined independently from biomass increments and turnover.     

Modelling radiation fluxes in simple and complex environments—application of the RayMan model

The most important meteorological parameter affecting the human energy balance during sunny weather conditions is the mean radiant temperature T_mrt. It considers the uniform temperature of a surrounding surface giving off blackbody radiation, which results in the same energy gain of a human body given the prevailing radiation fluxes. This energy gain usually varies considerably in open space conditions. In this paper, the model ‘RayMan’, used for the calculation of short- and long-wave radiation fluxes on the human body, is presented. The model, which takes complex urban structures into account, is suitable for several applications in urban areas such as urban planning and street design. The final output of the model is, however, the calculated T_mrt, which is required in the human energy balance model, and thus also for the assessment of the urban bioclimate, with the use of thermal indices such as predicted mean vote (PMV), physiologically equivalent temperature (PET) and standard effective temperature (SET*). The model has been developed based on the German VDI-Guidelines 3789, Part II (environmental meteorology, interactions between atmosphere and surfaces; calculation of short- and long-wave radiation) and VDI-3787 (environmental meteorology, methods for the human-biometeorological evaluation of climate and air quality for urban and regional planning. Part I: climate). The validation of the results of the RayMan model agrees with similar results obtained from experimental studies.     

Review of the ecology of Australian urban fauna: A focus on spatially explicit processes

Abstract Cities have a major impact on Australian landscapes, especially in coastal regions, to the detriment of native biodiversity. Areas suitable for urban development often coincide with those areas that support high levels of species diversity and endemism. However, there is a paucity of reliable information available to guide urban conservation planning and management, especially regarding the trade‐off between investing in protecting and restoring habitat at the landscape level, and investing in programmes to maintain the condition of remnant vegetation at the local (site) level. We review the literature on Australian urban ecology, focusing on urban terrestrial and aquatic vertebrate and invertebrate fauna. We identify four main factors limiting our knowledge of urban fauna: (i) a lack of studies focusing at multiple ecological levels; (ii) a lack of multispecies studies; (iii) an almost total absence of long‐term (temporal) studies; and (iv) a need for stronger integration of research outcomes into urban conservation planning and management. We present a set of key principles for the development of a spatially explicit, long‐term approach to urban fauna research. This requires an understanding of the importance of local‐level habitat quality and condition relative to the composition, configuration and connectivity of habitats within the larger urban landscape. These principles will ultimately strengthen urban fauna management and conservation planning by enabling us to prioritize and allocate limited financial resources to maximize the conservation return.     

Targeted subfield switchgrass integration could improve the farm economy,water quality,and bioenergy feedstock production

Progress on reducing nutrient loss from annual croplands has been hampered by perceived conflicts between short‐term profitability and long‐term stewardship, but these may be overcome through strategic integration of perennial crops. Perennial biomass crops like switchgrass can mitigate nitrate‐nitrogen (NO₃‐N) leaching, address bioenergy feedstock targets, and – as a lower‐cost management alternative to annual crops (i.e., corn, soybeans) – may also improve farm profitability. We analyzed publicly available environmental, agronomic, and economic data with two integrated models: a subfield agroecosystem management model, Landscape Environmental Assessment Framework (LEAF), and a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC). We constructed a factorial combination of profitability and NO₃‐N leaching thresholds and simulated targeted switchgrass integration into corn/soybean cropland in the agricultural state of Iowa, USA. For each combination, we modeled (i) area converted to switchgrass, (ii) switchgrass biomass production, and (iii) NO₃‐N leaching reduction. We spatially analyzed two scenarios: converting to switchgrass corn/soybean cropland losing >US$ 100 ha^?1 and leaching >50 kg ha^?1 (‘conservative’ scenario) or losing >US$ 0 ha^?1 and leaching >20 kg ha^?1 (‘nutrient reduction’ scenario). Compared to baseline, the ‘conservative’ scenario resulted in 12% of cropland converted to switchgrass, which produced 11 million Mg of biomass and reduced leached NO₃‐N 18% statewide. The ‘nutrient reduction’ scenario converted 37% of cropland to switchgrass, producing 34 million Mg biomass and reducing leached NO₃‐N 38% statewide. The opportunity to meet joint goals was greatest within watersheds with undulating topography and lower corn/soybean productivity. Our approach bridges the scales at which NO₃‐N loss and profitability are usually considered, and is informed by both mechanistic and empirical understanding. Though approximated, our analysis supports development of farm‐level tools that can identify locations where both farm profitability and water quality improvement can be achieved through the strategic integration of perennial vegetation.     

Precipitation‐drainage cycles lead to hot moments in soil carbon dioxide dynamics in a Neotropical wet forest

Soil CO₂ concentrations and emissions from tropical forests are modulated seasonally by precipitation. However, subseasonal responses to meteorological events (e.g., storms, drought) are less well known. Here, we present the effects of meteorological variability on short‐term (hours to months) dynamics of soil CO₂ concentrations and emissions in a Neotropical wet forest. We continuously monitored soil temperature, moisture, and CO₂ for a three‐year period (2015–2017), encompassing normal conditions, floods, a dry El Niño period, and a hurricane. We used a coupled model (Hydrus‐1D) for soil water propagation, heat transfer, and diffusive gas transport to explain observed soil moisture, soil temperature, and soil CO₂ concentration responses to meteorology, and we estimated soil CO₂ efflux with a gradient‐flux model. Then, we predicted changes in soil CO₂ concentrations and emissions under different warming climate change scenarios. Observed short‐term (hourly to daily) soil CO₂ concentration responded more to precipitation than to other meteorological variables (including lower pressure during the hurricane). Observed soil CO₂ failed to exhibit diel patterns (associated with diel temperature fluctuations in drier climates), except during the drier El Niño period. Climate change scenarios showed enhanced soil CO₂ due to warmer conditions, while precipitation played a critical role in moderating the balance between concentrations and emissions. The scenario with increased precipitation (based on a regional model projection) led to increases of +11% in soil CO₂ concentrations and +4% in soil CO₂ emissions. The scenario with decreased precipitation (based on global circulation model projections) resulted in increases of +4% in soil CO₂ concentrations and +18% in soil CO₂ emissions, and presented more prominent hot moments in soil CO₂ outgassing. These findings suggest that soil CO₂ will increase under warmer climate in tropical wet forests, and precipitation patterns will define the intensity of CO₂ outgassing hot moments.     

Uncertainty in predicting range dynamics of endemic alpine plants under climate warming

Correlative species distribution models have long been the predominant approach to predict species’ range responses to climate change. Recently, the use of dynamic models is increasingly advocated for because these models better represent the main processes involved in range shifts and also simulate transient dynamics. A well‐known problem with the application of these models is the lack of data for estimating necessary parameters of demographic and dispersal processes. However, what has been hardly considered so far is the fact that simulating transient dynamics potentially implies additional uncertainty arising from our ignorance of short‐term climate variability in future climatic trends. Here, we use endemic mountain plants of Austria as a case study to assess how the integration of decadal variability in future climate affects outcomes of dynamic range models as compared to projected long‐term trends and uncertainty in demographic and dispersal parameters. We do so by contrasting simulations of a so‐called hybrid model run under fluctuating climatic conditions with those based on a linear interpolation of climatic conditions between current values and those predicted for the end of the 21st century. We find that accounting for short‐term climate variability modifies model results nearly as differences in projected long‐term trends and much more than uncertainty in demographic/dispersal parameters. In particular, range loss and extinction rates are much higher when simulations are run under fluctuating conditions. These results highlight the importance of considering the appropriate temporal resolution when parameterizing and applying range‐dynamic models, and hybrid models in particular. In case of our endemic mountain plants, we hypothesize that smoothed linear time series deliver more reliable results because these long‐lived species are primarily responsive to long‐term climate averages.     

Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data

Forecasting pollen concentrations in the short term is a topic of major importance in aerobiology. Forecasting models proposed in the literature are numerous and increasingly complex, but they fail in at least 25 % of cases and are not available for all botanical species. This work makes it possible to build a forecast model from meteorological data for estimating pollen concentration over a certain threshold of Poaceae, an allergenic family. In Italy, about 25 % of the population suffer from allergies, these in 80 % of cases being caused by airborne allergens, including taxa of agricultural interest such as Poaceae. The pollen dispersion in air is determined by both the phenological stage of plants and the meteorological conditions; the pollen presence varies according to the year, month and even the time of the day. There is a correlation between environmental factors, pollen concentrations and pollinosis. A partial least squares discriminant analysis approach was used in order to predict the presence of Poaceae pollen in the atmosphere with a time lag of 3, 5, 7 days, on the basis of a data set of 14 meteorological and pollen variables over a period of 14 years (1997–2010). The results show a high accuracy in predicting pollen critical concentrations, with values ranging from 85.4 to 88.0 %. This study is hopefully a positive first step in the use of a statistical approach that in the next future could have clinical applications.     

Collagen and keratin polypeptide models for assessing the natural and artificial protein decay of organic materials

Among the materials constituting the natural and cultural heritage, organic materials of proteinaceous origin as bone (collagen), parchment and woolen textiles (keratin) are the most susceptible to damage and decay because of their exposure to air pollution, inappropriate values of ambient temperature, humidity and light. Aiming at contributing to the development of a reliable and reproducible immunoassay for the evaluation of collagen and keratin decay, three polypeptide models of these proteins were designed, synthesized and studied. Polypeptide [Pro‐Ser(OBzl)‐Gly]_n incorporates the typical motif Pro‐X‐Gly of collagen; polypeptide [Pro‐Cys(Acm)‐Gly]_n is a model of the C‐terminal domain of type I keratin, corresponding to the repeating unit Pro‐Cys‐X of keratin, while polypeptide Ac‐YRSGGGFGYRSGGGFGYRS‐βAla‐NH₂ encloses the characteristic repeating sequence GGGFGYRS of the N‐terminal part of Type II keratin. These polypeptides may be considered as simplified models that mimic fragments of collagen and keratin resulting from artificial and natural ageing or decay. It is concluded that high recognition of anti‐polypeptide antibodies, produced after immunizations, by the bone, parchment and textile samples is indicative of high deterioration, while high anti‐collagen or anti‐keratin recognition is indicative of low deterioration. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.     

Fluorescence decay of dyed protozoa: differences between stressed and non‐stressed cysts

Several series of tests have shown that fresh, intact samples of Giardia duodenalis and Cryptosporidium parvum (oo)cysts are not marked by fluorescent probes such as carboxyfluorcein‐succinimidyl‐diacetate‐ester (CFDA‐SE), C12‐resazurin and SYTOX® Green, probably because of their robust cell walls. These dyes fail to indicate the viability of such protozoa and allow negative responses to be recorded from living and infectious samples. Cryptosporidium parvum showed stronger isolation from chemicals, with living oocysts remaining unstained by the probe for up to 90 days after extraction. However, in further fluorescence decay (FD) experiments run with G. duodenalis samples stained using CFDA‐SE (comprising living, non‐stressed but aged cysts, heat‐killed samples and UV‐C‐stressed samples) each showed a different FD decay profile, here studied in seven series of tests of five replicates each. The FD profiles were fitted by double‐exponential decay kinetics, with the decay constant k₂ being five times higher than k₁. This FD procedure is fast and can be easily reproduced in 10 steps, taking ~ 1 h of laboratory work for already purified samples. Copyright © 2015 John Wiley & Sons, Ltd.     

In Situ Formation of MoO3 in PEDOT:PSS Matrix: A Facile Way to Produce a Smooth and Less Hygroscopic Hole Transport Layer for Highly Stable Polymer Bulk Heterojunction Solar Cells

A solution‐processed neutral hole transport layer is developed by in situ formation of MoO₃ in aqueous PEDOT:PSS dispersion (MoO₃‐PEDOT:PSS). This MoO₃‐PEDOT:PSS composite film takes advantage of both the highly conductive PEDOT:PSS and the ambient conditions stability of MoO₃; consequently it possesses a smooth surface and considerably reduced hygroscopicity. The resulting bulk heterojunction polymer solar cells (BHJ PSC) based on poly[2,3‐bis‐(3‐octyloxyphenyl)quinoxaline‐5,8‐diyl‐alt‐thiophene‐2,5‐diyl] (TQ1):[6,6]‐phenyl‐C₇₁‐butyric acid methyl ester (PC₇₀BM) blends using MoO₃‐PEDOT:PSS composite film as hole transport layer (HTL) show considerable improvement in power conversion efficiency (PCE), from 5.5% to 6.4%, compared with the reference pristine PEDOT:PSS‐based device. More importantly, the device with MoO₃‐PEDOT:PSS HTL shows considerably improved stability, with the PCE remaining at 80% of its original value when stored in ambient air in the dark for 10 days. In comparison, the reference solar cell with PEDOT:PSS layer shows complete failure within 10 days. This MoO₃‐PEDOT:PSS implies the potential for low‐cost roll‐to‐roll fabrication of high‐efficiency polymer solar cells with long‐term stability at ambient conditions.     

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

Understanding large‐scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County‐level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short‐term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county‐level corn yields. By conflating heterogeneous phenology‐based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology‐based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end‐of‐the‐season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root‐mean‐square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high‐dimensional (spectral, spatial, and temporal) input features to achieve a robust county‐level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.     

A large proportion of North American net ecosystem production is offset by emissions from harvested products,river/stream evasion,and biomass burning

Diagnostic carbon cycle models produce estimates of net ecosystem production (NEP, the balance of net primary production and heterotrophic respiration) by integrating information from (i) satellite‐based observations of land surface vegetation characteristics; (ii) distributed meteorological data; and (iii) eddy covariance flux tower observations of net ecosystem exchange (NEE) (used in model parameterization). However, a full bottom‐up accounting of NEE (the vertical carbon flux) that is suitable for integration with atmosphere‐based inversion modeling also includes emissions from decomposition/respiration of harvested forest and agricultural products, CO₂ evasion from streams and rivers, and biomass burning. Here, we produce a daily time step NEE for North America for the year 2004 that includes NEP as well as the additional emissions. This NEE product was run in the forward mode through the CarbonTracker inversion setup to evaluate its consistency with CO₂ concentration observations. The year 2004 was climatologically favorable for NEP over North America and the continental total was estimated at 1730 ± 370 TgC yr^?1 (a carbon sink). Harvested product emissions (316 ± 80 TgC yr^?1), river/stream evasion (158 ± 50 TgC yr^?1), and fire emissions (142 ± 45 TgC yr^?1) counteracted a large proportion (35%) of the NEP sink. Geographic areas with strong carbon sinks included Midwest US croplands, and forested regions of the Northeast, Southeast, and Pacific Northwest. The forward mode run with CarbonTracker produced good agreement between observed and simulated wintertime CO₂ concentrations aggregated over eight measurement sites around North America, but overestimates of summertime concentrations that suggested an underestimation of summertime carbon uptake. As terrestrial NEP is the dominant offset to fossil fuel emission over North America, a good understanding of its spatial and temporal variation – as well as the fate of the carbon it sequesters ─ is needed for a comprehensive view of the carbon cycle.     

Distance decay of similarity among European urban floras: the impact of anthropogenic activities on β diversity

Aim We examine how two categories of non‐native species (archaeophyte and neophyte, introduced before and after ad 1500, respectively) have had different impacts on β diversity across European urban floras. Our goal is to use the unique biological perspective provided by urban areas, and the contrasting historical and geographical perspectives provided by archaeophytes and neophytes, to infer how non‐native species will impact upon β diversity in the future. Location Twenty‐two urban areas located in seven European countries. Methods We used the β‐sim dissimilarity index to estimate the level of β diversity for 231 unique pair‐wise combinations of 22 urban floras. We examined bivariate plots of dissimilarity by geographical separation of city centres to evaluate distance decay of similarity for native species, archaeophytes and neophytes. Results Based on average percentages, 52.8% (SD = 8.2%) of species in the urban floras were identified as non‐native with 28.3% (SD = 6.9%) classified as neophytes and 24.5% (SD = 4.9%) as archaeophytes. Relative to native species, across urban floras, archaeophytes were associated with higher compositional similarity and weaker distance decay patterns, whereas neophytes were associated with lower compositional similarity and stronger distance decay patterns. Main conclusions Across European urban floras, archaeophytes and neophytes occurred in similar numbers but archaeophytes were consistently associated with lower β diversity and neophytes with higher β diversity. Thus, the impact of non‐native species on β diversity can be determined, at least in part, through their historical and geographical associations with anthropogenic activities. If archaeophytes represent the long‐term biogeographical outcome for human commensal species, neophytes could develop similar patterns. The consequences, however, are likely to be more substantial ecologically and geographically due to the increasing numbers of neophytes and their global anthropogenic associations. Nevertheless, at present, our findings suggest that, based on occurrence information, neophytes have not achieved this state with European urban floras retaining regionally distinct assemblages of neophytes.     

City limits: Heat tolerance is influenced by body size and hydration state in an urban ant community

Cities are rapidly expanding, and global warming is intensified in urban environments due to the urban heat island effect. Therefore, urban animals may be particularly susceptible to warming associated with ongoing climate change. We used a comparative and manipulative approach to test three related hypotheses about the determinants of heat tolerance or critical thermal maximum (CT_max) in urban ants—specifically, that (a) body size, (b) hydration status, and (c) chosen microenvironments influence CT_max. We further tested a fourth hypothesis that native species are particularly physiologically vulnerable in urban environments. We manipulated water access and determined CT_max for 11 species common to cities in California's Central Valley that exhibit nearly 300‐fold variation in body size. There was a moderate phylogenetic signal influencing CT_max, and inter (but not intra) specific variation in body size influenced CT_max where larger species had higher CT_max. The sensitivity of ants’ CT_max to water availability exhibited species‐specific thresholds where short‐term water limitation (8 hr) reduced CT_max and body water content in some species while longer‐term water limitation (32 hr) was required to reduce these traits in other species. However, CT_max was not related to the temperatures chosen by ants during activity. Further, we found support for our fourth hypothesis because CT_max and estimates of thermal safety margin in native species were more sensitive to water availability relative to non‐native species. In sum, we provide evidence of links between heat tolerance and water availability, which will become critically important in an increasingly warm, dry, and urbanized world that others have shown may be selecting for smaller (not larger) body size.     

粤港澳大湾区PM2.5时空分布特征及其与气象要素的关系

细颗粒物（PM_2.5）污染不仅是现代社会城市化进程中的痛点,也是城市大气环境研究不可忽略的重要焦点。粤港澳大湾区作为世界级城市群,既是城市区域经济社会文化发展的重要体现,更是国家区域发展战略的重要构成与政策实施落脚点,其生态环境的优劣尤其受瞩目。对1999-2016年大湾区地表PM_2.5浓度栅格数据集进行了时空分布特征分析,其中空间自相关分析选取莫兰指数（Moran''I指数）作为度量;并利用多元线性回归模型探讨研究区内PM_2.5与气象要素之间关系。结果表明：粤港澳大湾区1999-2016年历年PM_2.5浓度呈先增加后减小的趋势,2008年为时间拐点,该时间节点之后空气质量显著提高,且1999、2009、2016三年,年平均PM_2.5浓度相似值趋于聚集分布。冷热点分析结果表明：热点区域集中于湾区行政核心区域范围内;冷点集中于核心边缘区域,空气质量较优。利用皮尔森相关分析最终筛选出实际蒸散量（aet）、太阳辐射（srad）、最低温度（tmmn）、蒸汽压（vap）、饱和水汽压差（vpd）、风速（ws）等6个气象因子,利用回归分析判断影响PM_2.5浓度时空分布的显著因子。结果表明：本研究区太阳辐射与PM_2.5浓度关系呈负相关,该结果与其他城市相关研究有较大差异,最小温度与PM_2.5浓度呈正相关,风速与PM_2.5浓度呈负相关,饱和水气压差与PM_2.5浓度呈正相关。     

Substitutional Growth of Methylammonium Lead Iodide Perovskites in Alcohols

Methylammonium lead iodide (MAPbI₃) perovskites are organic–inorganic semiconductors with long carrier diffusion lengths serving as the light‐harvesting component in optoelectronics. Through a substitutional growth of MAPbI₃ catalyzed by polar protic alcohols, evidence is shown for their substrate‐ and annealing‐free production and use of toxic solvents and high temperature is prevented. The resulting variable‐sized crystals (≈100 nm–10 µm) are found to be tetragonally single‐phased in alcohols and precipitated as powders that are metallic‐lead‐free. A comparatively low MAPbI₃ yield in toluene supports the role of alcohol polarity and the type of solvent (protic vs aprotic). The theoretical calculations suggest that overall Gibbs free energy in alcohols is lowered due to their catalytic impact. Based on this alcohol‐catalyzed approach, MAPbI₃ is obtained, which is chemically stable in air up to ≈1.5 months and thermally stable (≤300 °C). This method is amendable to large‐scale manufacturing and ultimately can lead to energy‐efficient, low‐cost, and stable devices.     

Characterization of an urban-rural CO<Subscript>2</Subscript>/temperature gradient and associated changes in initial plant productivity during secondary succession

To examine the impact of climate change on vegetative productivity, we exposed fallow agricultural soil to an in situ temperature and CO₂ gradient between urban, suburban and rural areas in 2002. Along the gradient, average daytime CO₂ concentration increased by 21% and maximum (daytime) and minimum (nighttime) daily temperatures increased by 1.6 and 3.3°C, respectively in an urban relative to a rural location. Consistent location differences in soil temperature were also ascertained. No other consistent differences in meteorological variables (e.g. wind speed, humidity, PAR, tropospheric ozone) as a function of urbanization were documented. The urban-induced environmental changes that were observed were consistent with most short-term (~50 year) global change scenarios regarding CO₂ concentration and air temperature. Productivity, determined as final above-ground biomass, and maximum plant height were positively affected by daytime and soil temperatures as well as enhanced [CO₂], increasing 60 and 115% for the suburban and urban sites, respectively, relative to the rural site. While long-term data are needed, these initial results suggest that urban environments may act as a reasonable surrogate for investigating future climatic change in vegetative communities.     

Biotic degradation at night,abiotic degradation at day: positive feedbacks on litter decomposition in drylands

The arid and semi‐arid drylands of the world are increasingly recognized for their role in the terrestrial net carbon dioxide (CO₂) uptake, which depends largely on plant litter decomposition and the subsequent release of CO₂ back to the atmosphere. Observed decomposition rates in drylands are higher than predictions by biogeochemical models, which are traditionally based on microbial (biotic) degradation enabled by precipitation as the main mechanism of litter decomposition. Consequently, recent research in drylands has focused on abiotic mechanisms, mainly photochemical and thermal degradation, but they only partly explain litter decomposition under dry conditions, suggesting the operation of an additional mechanism. Here we show that in the absence of precipitation, absorption of dew and water vapor by litter in the field enables microbial degradation at night. By experimentally manipulating solar irradiance and nighttime air humidity, we estimated that most of the litter CO₂ efflux and decay occurring in the dry season was due to nighttime microbial degradation, with considerable additional contributions from photochemical and thermal degradation during the daytime. In a complementary study, at three sites across the Mediterranean Basin, litter CO₂ efflux was largely explained by litter moisture driving microbial degradation and ultraviolet radiation driving photodegradation. We further observed mutual enhancement of microbial activity and photodegradation at a daily scale. Identifying the interplay of decay mechanisms enhances our understanding of carbon turnover in drylands, which should improve the predictions of the long‐term trend of global carbon sequestration.     

Improving phenological forecasting models for rape stem weevil,Ceutorhynchus napi Gyll., based on long‐term multisite datasets

New threshold‐based models to predict the start of invasion by the stem‐boring pest, the rape stem weevil (Ceutorhynchus napi Gyll.) of winter oilseed rape (Brassica napus L.), were developed and compared to published models using long‐term datasets on weather and weevil phenology from experimental locations in Germany and Luxembourg. Threshold values for daily records of maximum air temperature, mean soil temperature, sunshine duration and total precipitation were adjusted to local conditions on the date of first weevil migration in spring. Mean error and the root mean squared error were used to assess model quality, where the error is defined as the number of days between predicted and observed arrival of weevils on the crop (regardless of sign). Best model results predicted first crop invasion by rape stem weevil when the thresholds of daily maximum air temperature ≥7.8°C, mean soil temperature ≥6.6°C, daily total precipitation ≤1.0 mm and sunshine duration ≥1 h were matched. This model takes into account meteorological variables likely to influence conditions at the overwintering site of the weevils in the soil, as well as variables that may limit weevil flight. Adjusted air temperature threshold values were consistently lower for Luxembourg sites than for those optimized for Germany. A simple model relating the date of first weevil invasion to accumulated daily maximum air temperature above 0°C (from 1 January) was also evaluated. This proved less suitable for forecasting crop invasion by C. napi. We suggest that phenological models using locally adjusted meteorological‐based thresholds have the potential to offer sufficiently accurate forecasts of first immigration flights by C. napi for appropriate timing of insecticide application. In addition, the developed models are suitable tools to be used in climate change impact studies.