Resolving the Formation Mechanism of HONO via Ammonia-Promoted Photosensitized Conversion of Monomeric NO2 on Urban Glass Surfaces

Understanding the formation processes of nitrous acid (HONO) is crucial due to its role as a primary source of hydroxyl radicals (OH) in the urban atmosphere and its involvement in haze events. In this study, we propose a new pathway for HONO formation via the UVA-light-promoted photosensitized conversion of nitrogen dioxide (NO₂) in the presence of ammonia (NH₃) and polycyclic aromatic hydrocarbons (PAHs, common compounds in urban grime). This new mechanism differs from the traditional mechanism, as it does not require the formation of the NO₂ dimer. Instead, the enhanced electronic interaction between the UVA-light excited triplet state of PAHs and NO₂-H₂O/NO₂-NH₃-H₂O significantly reduces the energy barrier and facilitates the exothermic formation of HONO from monomeric NO₂. Furthermore, the performed experiments confirmed our theoretical findings and revealed that the synergistic effect from light-excited PAHs and NH₃ boosts the HONO formation with determined HONO fluxes of 3.6 × 10¹⁰ molecules cm^-2 s^-1 at 60% relative humidity (RH) higher than any previously reported HONO fluxes. Intriguingly, light-induced NO₂ to HONO conversion yield on authentic urban grime in presence of NH₃ is unprecedented 130% at 60% RH due to the role of NH₃ acting as a hydrogen carrier, facilitating the transfer of hydrogen from H₂O to NO₂. These results show that NH₃-assisted UVA-light-induced NO₂ to HONO conversion on urban surfaces can be a dominant source of HONO in the metropolitan area.

Modeling Litter Stocks in Planted Forests of Northern Mexico

Litter, LS, is the organic material in which locates in the top A soil horizon, playing key ecological roles in forests. Models, in contrast to common allocation factors, must be used in LS assessments as they are currently absent in the scientific literature. Its evaluation assess the mass, input and flux of several bio-geo-chemicals, rainfall interception as one component of the local hydrology, and wildfire regimes, among others, hence its importance in forestry. The aim of this study was to: (i) develop models to assess LS, accumulation, and loss rates; and (ii) assess rainfall interception and fire regimes in 133 northern forest plantations of Mexico. Two developed techniques: the statistical model (SMLS) and the mass balance budget model (MBMLS) tested and validated local and regional LS datasets. Models use basal area, timber, aboveground tree biomass, litter fall, accumulation, and loss sub-models. The best fitting model was used to predict rainfall interception and fire behavior in forest plantations. Results showed the SMLS model predicted and validated LS datasets (p = 0.0001; r2 = 0.82 and p = 0.0001; r2 = 0.79) better than the MBMLS model (p = 0.0001; r2 = 0.32 and p = 0.0001; r2 = 0.66) but the later followed well tendencies of Mexican and World datasets; counts for inputs, stocks, and losses from all processes and revealed decomposition loss may explain ≈40% of the total LS variance. SMLS predicted forest plantations growing in high productivity 40-year-old stands accumulate LS > 30 Mg ha−1 shifting to the new high-severity wildfire regime and intercepting ≈15% of the annual rainfall. SMLS is preliminarily recommended for LS assessments and predicts the need of LS management in forest plantations (>40-year-old) to reduce rainfall interception as well as the risk of high-severity wildfires. The novel, flexible, simple, contrasting and consistent modeling approaches is a piece of scientific information required in forest management.

Estimating daily evapotranspiration in the agricultural-pastoral ecotone in Northwest China: A comparative analysis of the Complementary Relationship, WRF-CLM4.0, and WRF-Noah methods

Accurate estimation of evapotranspiration (ET) over regional scale is essential for ecohydrological research, agricultural production, and water resources management. However, few studies have been done to estimate regional ET in data lacking, highly heterogeneous arid areas such as the Agricultural-Pastoral Ecotone in Northwest China (APENC). In this study, we compared three actual ET-estimation methods driven by Weather Research and Forecasting (WRF) model in a semi-arid region. We selected the state of the art Weather Research and Forecasting-Community Land Model 4.0 (WRF-CLM4.0) model, the widely used WRF-Noah model and an empirical Complementary Relationship (CR) model to compare their model structures and mechanisms of estimating daily ET in the study region. The WRF model was chosen to address the problem of data scarcity in the study region and to derive model input for ET estimation with high spatial resolution. The seasonal and pooled performances of the three models were verified with in situ observations. Results indicate that the WRF-CLM4.0 model shows a better applicability in the study region, with a superior performance for the pooled datasets (Pearson correlation coefficient [r] = 0.89, root-mean-square error [RMSE] = 0.66 mm/d and Nash-Sutcliffe efficiency coefficient [NSE] = 0.90), while the CR model has a comparable performance (r = 0.91, RMSE = 0.86 mm/d and NSE = 0.85) and the WRF-Noah model shows the worst performance (r = 0.82, RMSE = 0.94 mm/d and NSE = 0.81). The differences are mainly caused by different representations of the land surface characteristics and hydrology of the study region by the three different models. Our analysis shows that the WRF-CLM4.0 model and the CR model are more applicable to the APENC than the WRF-Noah model. For regional applications, the CR model, with fewer parameters and simpler structure, is able to capture the local characteristic and well-suited for data lacking, highly heterogeneous landscapes such as the APENC.

Winter-Active Spider Fauna is Affected by Plantation Forest Type

Plantations of non-native trees for commercial use are common practice in Europe. They are known to have severe ecological impacts on arthropod fauna by altering microclimatic conditions and reducing microhabitat diversity. However, the effect of plantation tree species on winter-active fauna is relatively unknown. Spiders are a diverse predatory arthropod taxon with strong effect on their prey populations. The composition of spider communities sensitively indicates changes in habitat structure. We established 40 sampling sites in five non-native pine and five native poplar plantations and collected spiders with pitfall traps for two winters in the Southern part of Hungary. We assessed the average height of vegetation and percentage cover of leaf litter, mosses, herbaceous vegetation, and shrubs to characterize habitat structure. We found species richness and activity density of spiders in the non-native compared to the native plantations, presumably due to the more temperate microclimate in pine than in poplar plantations. However, there was no significant effect of habitat structure and its interaction with forest type on species richness and activity density of spiders. Species composition of non-native and native plantation forests differed significantly. Furthermore, we identified six characteristic spider species of non-native plantations with preference for relatively moist habitat conditions. The single characteristic species, (Agroeca cuprea Menge, 1873) for the native plantations preferred dry and partly shaded habitats. We conclude that the effect of microclimatic differences and prey availability presumably overrides the effect of habitat structure on winter-active spiders.

Effects of Different Weeding Methods on the Biomass of Vegetation and Soil Evaporation in Eucalyptus Plantations

Eucalyptus is a fast-growing, short-cycle, and high-efficiency tree species that is widely planted all over the world. Weeding is a special practice for Eucalyptus plantations that aims to cultivate seedlings and reduce vegetation competition for nutrients. In this study, a typical Eucalyptus plantation was selected as the research object, and the effects of two different weeding methods on soil evaporation and plant growth were studied. The results showed that mechanical weeding could effectively remove harmful weeds at an early stage, but after 30 days of weeding, all kinds of plants gradually recovered; herbicide weeding required long-term maintenance. The herbicide had the best control effect on Microstegium vagans (Nees ex Steud.) A. Camus, Dicranopteris dichotoma (Thunb.) Bernh, and Blechnum orientale, but the effect on shrubs was not obvious. The evaporation rate of soil increased rapidly within five days after mechanical weeding. After 139–200 days of weeding, the differences in evaporation between non-weeding, herbicide weeding, and mechanical weeding was decreased. The average daily evaporation was 0.52, 0.48, and 051 mm/d under these three practices. Meanwhile, weeding could promote Eucalyptus growth. Our results showed that weeding could significantly increase the height, diameter at breast height, and volume of Eucalyptus. One month after herbicide weeding or mechanical weeding (July), the height, DBH, and volume of Eucalyptus were significantly greater than those under non-weeding, but there was no significant difference between herbicide weeding and mechanical weeding. In addition, according to the different initial moisture contents, the evaporation rate increased with increasing initial moisture content and showed a very significant correlation.

High-resolution simulation and validation of soil moisture in the arid region of Northwest China

Soil moisture plays an important role in land-atmosphere interactions, agricultural drought monitoring, and water resource management, particularly across arid regions. However, it is challenging to simulate soil moisture of high spatial resolution and to evaluate soil moisture at fine spatial resolution in arid regions in Northwest China due to considerable uncertainties in forcing data and limited in situ measurements. Then, the data set was used to produce the 1 km high-resolution atmospheric forcing datasets and to drive the Community Land Model version 3.5 (CLM3.5) for simulating spatiotemporally continuous surface soil moisture. The capabilities of soil moisture simulation using CLM3.5 forced by the XJLDAS-driven field were validated against data obtained at three soil layers (0-10, 0-20, and 0-50 cm) from 54 soil moisture stations in Xinjiang. Results show that the simulated soil moisture agreed well with the observations [CORR > 0.952], and the intra-annual soil moisture in Xinjiang gradually increased during May through August. The main factors that affect changes in soil moisture across the study region were precipitation and snowmelt. The overall finding of this study is that an XJLDAS, high-resolution forcing data driven CLM3.5 can be used to generate accurate and continuous soil moisture of high resolution (1km) in Xinjiang. This study can help understand the spatiotemporal features of the soil moisture, and provide important input for hydrological studies and agricultural water resources management over the arid region.

New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model

Ammonia surface flux is bidirectional; that is, net flux can be either upward or downward. In fertilized agricultural croplands and grasslands there is usually more emission than deposition especially in midday during warmer seasons. In North America, most of the ammonia emissions are from agriculture with a significant fraction of that coming from fertilizer. A new bidirectional ammonia flux modeling system has been developed in the Community Multiscale Air Quality (CMAQ) model, which has close linkages with the Environmental Policy Integrated Climate (EPIC) agricultural ecosystem model. Daily inputs from EPIC are used to calculate soil ammonia concentrations that are combined with air concentrations in CMAQ to calculate bidirectional surface flux. The model is evaluated against surface measurements of NH₃ concentrations, NH₄ ⁺ and SO₄ ^2- aerosol concentrations, NH₄ ⁺ wet deposition measurements, and satellite retrievals of NH₃ concentrations. The evaluation shows significant improvement over the base model without bidirectional ammonia flux. Comparisons to monthly average satellite retrievals show similar spatial distribution with the highest ammonia concentrations in the Central Valley of California (CA), the Snake River valley in Idaho, and the western High Plains. In most areas the model underestimates, but in the Central Valley of CA, it generally overestimates ammonia concentration. Case study analyses indicate that modeled high fluxes of ammonia in CA are often caused by anomalous high soil ammonia loading from EPIC for particular crop types. While further improvements to parameterizations in EPIC and CMAQ are recommended, this system is a significant advance over previous ammonia bidirectional surface flux models.

Estimating land surface variables and sensitivity analysis for CLM and VIC simulations using remote sensing products

Assessment of Land Surface Models (LSMs) at heterogeneous terrain and climate regimes is essential for understanding complex hydrological and biophysical parameterization. This study utilized the two LSMs, Community Land Model (CLM 4.0) and three layer Variable Infiltration Capacity (VIC-3L), to estimate the interaction between land surface and atmosphere by means of energy fluxes including net radiation (R_N), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G). The modeled energy fluxes were analyzed at two sites: Freeman Ranch-2 (FR2) located in the lowland region of Texas (272m), and Providence 301 (P301) located on the mountains of Sierra Nevada in California (2015m) from 2003 to 2013. R_N was underestimated by CLM with bias -25.06Wm^-2 due to its snow hydrology scheme at P301. LE was overestimated by the VIC during summer precipitation and had a positive bias of 5.51Wm^-2, whereas CLM showed a negative bias of -6.58Wm^-2 at the FR2 site. G was considered as a residual term in CLM, which caused weak performance at P301, while VIC calculated G as a function of soil temperature, depth, and hydraulic conductivity. In addition, The MOD16 showed similar results with models at FR2; however, at P301, they yielded a correlation value of 0.85 and 0.21 for LSMs and MOD16, respectively. The later has lower correlation with in situ specifically in summer season caused by erroneous biophysical or meteorological inputs to the algorithms. The sensitivity analysis between soil moisture and turbulent fluxes, exhibited negative trend (especially for LE at P301) due to topography and snow cover. The results from this study are conducive to improvements in models and satellite based characterization of water and energy fluxes, especially at rugged terrain with high elevation, where observational experiments are difficult to conduct.

Half-Century Ammonia Emissions From Agricultural Systems in Southern Asia: Magnitude, Spatiotemporal Patterns, and Implications for Human Health

Much concern has been raised about the increasing threat to air quality and human health due to ammonia (NH3) emissions from agricultural systems, which is associated with the enrichment of reactive nitrogen (N) in southern Asia (SA), home of more than 60% the world's population (i.e., the people of West, central, East, South, and Southeast Asia). Southern Asia consumed more than half of the global synthetic N fertilizer and was the dominant region for livestock waste production since 2004. Excessive N application could lead to a rapid increase of NH3 in the atmosphere, resulting in severe air and water pollution in this region. However, there is still a lack of accurate estimates of NH3 emissions from agricultural systems. In this study, we simulated the agricultural NH3 fluxes in SA by coupling the Bidirectional NH3 exchange module (Bi-NH3) from the Community Multi-scale Air Quality model with the Dynamic Land Ecosystem Model. Our results indicated that NH3 emissions were 21.3 ± 3.9 Tg N yr-1 from SA agricultural systems with a rapidly increasing rate of ~0.3 Tg N yr-2 during 1961-2014. Among the emission sources, 10.8 Tg N yr-1 was released from synthetic N fertilizer use, and 10.4 ± 3.9 Tg N yr-1 was released from manure production in 2014. Ammonia emissions from China and India together accounted for 64% of the total amount in SA during 2000-2014. Our results imply that the increased NH3 emissions associated with high N inputs to croplands would likely be a significant threat to the environment and human health unless mitigation efforts are applied to reduce these emissions.

Investigating spatiotemporal changes of the land-surface processes in Xinjiang using high-resolution CLM3.5 and CLDAS: Soil temperature

Soil temperature plays a key role in the land surface processes because this parameter affects a series of physical, chemical, and biological processes in the soil, such as water and heat fluxes. However, observation of soil temperature is quite limited, especially at the regional scale. Therefore, this study is to investigate the spatiotemporal features of soil temperature in Xinjiang, China, using the Community Land model 3.5 (CLM3.5) with the atmospheric near-surface forcing data of the China Meteorological Administration Land Data Assimilation System (CLDAS). We use the observed soil temperature data collected from 105 national automatic stations during 2009 through 2012 in the study area to verify the simulation capability. The comparison results indicate that the CLM3.5 with the CLDAS driving field could well simulate the spatiotemporal patterns of the soil temperature at hourly, daily, and monthly time scales and at three depths (5 cm, 20 cm, and 80 cm). We also produce a soil temperature database of the region that is continuous both in time and space with high resolution (about 6.25 km). Overall, this study could help understand the regional and vertical characteristics of the soil temperature and provide an important scientific basis for other land-surface processes.

Climate-driven uncertainties in modeling terrestrial energy and water fluxes: a site-level to global-scale analysis

We used a land surface model constrained using data from flux tower sites, to analyze the biases in ecosystem energy and water fluxes arising due to the use of meteorological reanalysis datasets. Following site-level model calibration encompassing major vegetation types from the tropics to the northern high-latitudes, we repeated the site and global simulations using two reanalysis datasets: the NCEP/NCAR and the CRUNCEP. In comparison with the model simulations using observed meteorology from sites, the reanalysis-driven simulations produced several systematic biases in net radiation (Rn ), latent heat (LE), and sensible heat (H) fluxes. These include: (i) persistently positive tropical/subtropical biases in Rn using the NCEP/NCAR, and gradually transitioning to negative Rn biases in the higher latitudes; (ii) large positive H biases in the tropics/subtropics using the NCEP/NCAR; (iii) negative LE biases using the NCEP/NCAR above 40°N; (iv) high tropical LE using the CRUNCEP in comparison with observationally derived global estimates; and (v) flux-partitioning biases from canopy and ground components. Across vegetation types, we investigated the role of the meteorological drivers (shortwave and longwave radiation, atmospheric humidity, temperature, precipitation) and their seasonal biases in controlling these reanalysis-driven uncertainties. At the global scale, our site-level analysis explains several model-data differences in the LE and H fluxes when compared with observationally derived global estimates of these fluxes. Using our results, we discuss the implications of site-level model calibration on subsequent regional/global applications to study energy and hydrological processes. The flux-partitioning biases presented in this study have potential implications on the couplings among terrestrial carbon, energy, and water fluxes, and for the calibration of land-atmosphere parameterizations that are dependent on LE/H partitioning.