Observation Sites Research Articles

Overwintering diapause and survival of western Leucotaraxis argenticollis, a promising biological control agent for Adelges tsugae, in the eastern United States

Successful use of biological control agents can be improved with a thorough understanding of the life history, survival, diapausing strategy, and synchrony with the host in the introduced range. Introduction of a novel organism can be influenced by abiotic factors that can impact the interactions with their target prey. Leucotaraxis argenticollis (Zetterstedt) and Le. piniperda (Malloch) (Diptera: Chamaemyiidae) are two species of predatory fly showing promise in the biological control program to manage the invasive hemlock woolly adelgid, Adelges tsugae (Annand)(Hemiptera: Adelgidae), in eastern North America. Field releases of these Leucotaraxis spp. began in 2015, but little is known about their potential for survival or how they overwinter. In an effort to explore Le. argenticollis establishment potential, laboratory reared puparia were observed at seven sites, across a latitudinal gradient, on the east coast and one site on the west coast (native range) of North America from July 20, 2020, to June 1, 2021. Leucotaraxis piniperda puparia were included at five sites and observed to be diapausing in a different stage or using a different cue. Adult Le. argenticollis emerged in spring 2021, after successfully overwintering as puparia, with a subset emerging in summer-fall 2020 at all observation sites. Results suggest that 1) the interaction of mean daily fall maximum temperatures and relative humidity has the most impact on survival, 2) mean daily minimum fall temperatures influence induction/maintenance of diapause, and 3) mean average daily fall temperatures and relative humidity impact survival to spring emergence. Substantial variation in survival and facultative diapause of Le. argenicollis was observed along a latitudinal gradient, potentially influencing predator establishment, supporting the need for continued research.

Changes in Soil Freeze Depth in Response to Climatic Factors in the High-Latitude Regions of Northeast China

The changing climate in recent decades has caused rapid, widespread frozen ground degradation of the cryosphere, and changes in frozen ground may largely affect the ecosystem function. This study presents the long-term shrinkage of frozen ground, driven forces and resultant ecological impacts at three high-latitude sites (namely Nenjiang, Jiagedaqi, and Tahe, from low to high latitude) in Northeast China, an area that has received less attention in previous permafrost studies. As the key indicators of frozen ground, the ground surface and subsurface soil temperature (down to 40 cm) were found to increase in the period from the 1970s to the 2000s. The annual mean air temperatures at observation sites have increased significantly at rates of 0.24~0.40 °C/decade from 1971 to 2019, and were found to be negatively correlated to the change in soil freeze depth at the Jiagedaqi and Tahe sites. The annual snow depth at Jiagedaqi was found to decrease at a rate of 1.8 cm/decade, and positively correlate to the soil freezing index. The vegetation status of the three watersheds improved, as indicated by the increased normalized difference vegetation index (NDVI) from 2000 to 2017. Our results can make a valuable contribution to knowledge of climate–frozen ground interactions, and can highlight the importance of examining such interaction for permafrost protection in similar high-latitude permafrost-dominated basins.

The Roman snail (Gastropoda: Helicidae) is not a generalist herbivore, but shows food preferences for Urtica dioica and plant litter

ABSTRACT The food preferences and palatability of different plant species for snail and slug species in arable fields has been a major area of research in the past. The assumed diet of Helix pomatia (Linnaeus, 1758), however, provides an example of the limited knowledge of snail diets in nonagricultural habitats. This species has been described as a generalist herbivore, although it showed preferences for individual plant species in another study. Helix pomatia is considered a pest species, while also being listed as endangered in several Red Lists. In this study, the feeding activity of eight subpopulations of H. pomatia were recorded in a nature reserve in Cottbus, Germany, from April to October 2021 to understand food preferences and feeding behaviour. Along with the plant species on which feeding was observed, the height of feeding in the vegetation, shell width and height, number of whorls, and exposure to direct sunlight at observation sites were recorded. Results indicate that H. pomatia prefers Urtica dioica, and plant litter, which accounted for 35% and 44% of all observed feeding interactions respectively, as food resources, while generally only feeding on 27 of 143 available plant species. The importance of U. dioica as a food resource for 19 other terrestrial snail species is confirmed by 18 previous studies. In H. pomatia, analysis of the width and height of shells indicated that smaller, juvenile snails consumed plant material significantly higher up in vegetation as compared to adult snails, which fed closer to the ground. Awareness of the preference in H. pomatia for U. dioica as a food resource may be beneficial for land managers regarding the impact of this snail as an agriculture pest to crops. Urtica dioica is a very important food plant for terrestrial snails, and could play an important role in future species conservation strategies.

Analysis of the Applicability of Multisource Meteorological Precipitation Data in the Yunnan-Kweichow-Plateau Region at Multiple Scales

Multisource meteorological precipitation products are an important way to make up for a lack of observation sites or a lack of precipitation data in areas with a complex topography. They have important value for local industrial, agricultural, and ecological water use calculations, as well as for water resource evaluation and management. The Yunnan-Kweichow Plateau is located in southwest China and has a relatively backward economy and few meteorological stations. At the same time, the terrain is dominated by mountain valleys, precipitation is greatly affected by the terrain, and meteorological data are lacking, making the calculation of local water resources difficult. In this study, the applicability of the 3-hourly merged high-quality/IR estimates (3B42) of the Tropical Rainfall Measuring Mission (TRMM), China Meteorological Forcing Dataset (CMFD), and China Meteorological Assimilation Driving Datasets (CMADS) in the Yunnan-Kweichow Plateau was analyzed using multiple evaluation indicators of different temporal scales and precipitation intensity levels as well as the spatial distribution of the indicators based on measured daily precipitation data from 59 national meteorological basic stations in the study area in 2008–2018. The results showed that (1) the three products had performed well and could be applied to the calculation of local water resources with CMFD performing the best; (2) the performance of precipitation products was slightly worse on the daily scale, and the overall performance of the yearly, quarterly, and monthly scales was better; (3) good results were achieved in most regions, but there were also some regions with prominent overestimation and underestimation; (4) the three precipitation products had the highest probabilities of detection and the lowest false alarm rates for no rain and light rain, and the probability of detection gradually decreased with an increase in the precipitation intensity; and (5) the mean absolute error of precipitation products in rainy months is large, so the accuracy of products in the calculation of heavy rain and flood will be limited.

Cross-calibration method based on an automated observation site.

Cross-calibration methods are widely used in high-precision remote sensor calibrations and ensure observational consistency between sensors. Because two sensors must be observed under the same or similar conditions, the cross-calibration frequency is greatly reduced; performing cross-calibrations on Aqua/Terra MODIS, Sentinel-2A/Sentinel-2B MSI and other similar sensors is difficult due to synchronous-observation limitations. Additionally, few studies have cross-calibrated water-vapor-observation bands sensitive to atmospheric changes. In recent years, standard automated observation sites and unified processing technology networks, such as an Automated Radiative Calibration Network (RadCalNet) and an automated vicarious calibration system (AVCS), have provided automatic observation data and means for independently, continuously monitoring sensors, thus offering new cross-calibration references and bridges. We propose an AVCS-based cross-calibration method. By limiting the observational-condition differences when two remote sensors transit over wide temporal ranges through AVCS observation data, we improve the cross-calibration opportunity. Thereby, cross-calibrations and observation consistency evaluations between the abovementioned instruments are realized. The influence of AVCS-measurement uncertainties on the cross-calibration is analyzed. The consistency between the MODIS cross-calibration and sensor observation is within 3% (5% in SWIR bands); that for the MSI is within 1% (2.2% in the water-vapor-observation band); and for the cross-calibration of Aqua MODIS and the two MSI, the consistency between the cross-calibration-predicted TOA reflectance and the sensor-measured TOA reflectance was within 3.8%. Thus, the absolute AVCS-measurement uncertainty is also reduced, especially in the water-vapor-observation band. This method can be applied to cross-calibrations and measurement consistency evaluations of other remote sensors. Later, the spectral-difference influences on cross-calibrations will be further studied.

Physics-Informed Deep Learning for Reconstruction of Spatial Missing Climate Information in the Antarctic

Understanding the influence of the Antarctic on the global climate is crucial for the prediction of global warming. However, due to very few observation sites, it is difficult to reconstruct the rational spatial pattern by filling in the missing values from the limited site observations. To tackle this challenge, regional spatial gap-filling methods, such as Kriging and inverse distance weighted (IDW), are regularly used in geoscience. Nevertheless, the reconstructing credibility of these methods is undesirable when the spatial structure has massive missing pieces. Inspired by image inpainting, we propose a novel deep learning method that demonstrates a good effect by embedding the physics-aware initialization of deep learning methods for rapid learning and capturing the spatial dependence for the high-fidelity imputation of missing areas. We create the benchmark dataset that artificially masks the Antarctic region with ratios of 30%, 50% and 70%. The reconstructing monthly mean surface temperature using the deep learning image inpainting method RFR (Recurrent Feature Reasoning) exhibits an average of 63% and 71% improvement of accuracy over Kriging and IDW under different missing rates. With regard to wind speed, there are still 36% and 50% improvements. In particular, the achieved improvement is even better for the larger missing ratio, such as under the 70% missing rate, where the accuracy of RFR is 68% and 74% higher than Kriging and IDW for temperature and also 38% and 46% higher for wind speed. In addition, the PI-RFR (Physics-Informed Recurrent Feature Reasoning) method we proposed is initialized using the spatial pattern data simulated by the numerical climate model instead of the unified average. Compared with RFR, PI-RFR has an average accuracy improvement of 10% for temperature and 9% for wind speed. When applied to reconstruct the spatial pattern based on the Antarctic site observations, where the missing rate is over 90%, the proposed method exhibits more spatial characteristics than Kriging and IDW.

First quantification and chemical characterization of atmospheric microplastics observed in Seoul, South Korea

This study is the first report on atmospheric microplastics (MPs) observed in five outdoor environments, including an urban forest, a business center, commercial areas, and a public transportation hub in Seoul, South Korea. Air samples were collected using an active air pump sampler for 24 h in each area only on days without rainfall. All observed microplastics are secondary microplastics, in the form of irregularly-shaped fragments or fibers produced through various degradation processes, rather than being primarily produced like microbeads. The abundance of atmospheric MPs varied depending on the environment (i.e., region, height, and time) from 0.33 to 1.21 MP m-3, with the average number of MPs being 0.72 MP m-3 (standard deviation ± 0.39). MPs in the urban forest was observed to be 27% lower in abundance than that in the urban center which is ∼3 km away. The central business district was observed to have a 25% higher abundance during weekdays than on weekends. Our results show the ubiquity of MPs in various areas from high-rise buildings to forests tens of kilometers away from their direct sources, and a positive correlation between the abundance of MP and human activity. Morphologically, the fragment type (87.4%) predominated over the fiber type (12.6%), and chemically, polypropylene (PP) and polyethylene terephthalate (PET) components accounted for 65% of the total MP. PP polymers were found in all observation sites and contributed to 59% of the total MP fragments. The observed fibrous MPs were mainly composed of PET (72.7%) and PP (18.2%) polymers. Compared to other large cities (Shanghai, Beijing, Paris), Seoul is exposed to low levels of atmospheric MPs and high proportions of PP polymers. This study is limited to atmospheric MPs observed in summer and further investigation of MPs is needed to comprehensively understand the distribution and cycle of MPs based on long-term monitoring of atmospheric MPs.

Development of SLO KARST Near Fault Observatory site in SW Slovenia

The project RI-SI-EPOS (Research Infrastructure-Slovenia-European Plate Observing System) provided new scientific equipment for solid Earth science related to geology, seismology, geodesy and karstology. Karst research infrastructure is primarily used at the SLO KARST NFO (Near Fault Observatory) developing site. The area covers ~ 2600 km2 and is one of the most seismically active areas in SW Slovenia. It consists mostly of karstified Mesozoic carbonate rocks and partly of non-karstified Eocene flysch. The landscape has numerous karst features including caves, poljes, dolines, uvalas, karst springs, ponors and periodic karst lakes, with typical karst underground water drainage. Site development commenced with the installation of seven temporary seismic stations in the area, along with other geoscience equipment (gravimeter, 3D laser terrestrial scanner, GNSS antenna, TM72 extensometers, spectrometer for methane and drone), deployed in 2020. With the new dense seismic network, locations and other seismic parameters are already determined more reliably than in the past, contributing to a better understanding of active tectonic deformations at the junction between the seismically active Friuli region (Italy) and the Zagreb–Petrinja area (Croatia). The new solid Earth Geoscience research infrastructure is already collecting a big amount of data following the FAIR principles (making data Findable, Accessible, Interoperable and Reusable) aiming to be included in the national and international research databases. Here, the first results from the newly operating SLO KARST NFO temporary seismic network (May 2020–June 2021) are discussed.

Quantification of Central and Eastern China's atmospheric CH4 enhancement changes and its contributions based on machine learning approach

Methane is the second largest anthropogenic greenhouse gas, and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks. Therefore, the monitoring of CH4 concentration changes and the assessment of underlying driving factors can provide scientific basis for the government's policy making and evaluation. China is the world's largest emitter of anthropogenic methane. However, due to the lack of ground-based observation sites, little work has been done on the spatial-temporal variations for the past decades and influencing factors in China, especially for areas with high anthropogenic emissions as Central and Eastern China. Here to quantify atmospheric CH4 enhancements trends and its driving factors in Central and Eastern China, we combined the most up-to-date TROPOMI satellite-based column CH4 (xCH4) concentration from 2018 to 2022, anthropogenic and natural emissions, and a random forest-based machine learning approach, to simulate atmospheric xCH4 enhancements from 2001 to 2018. The results showed that (1) the random forest model was able to accurately establish the relationship between emission sources and xCH4 enhancement with a correlation coefficient (R²) of 0.89 and a root mean-square error (RMSE) of 11.98 ppb; (2)The xCH4 enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018, with a relative change of 3.27%±0.13%; (3) The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH4 enhancement, contributing 68.00% and 31.21%, respectively, and the decrease of animal ruminants contributed -6.70% of its enhancement trend.

Sky-brightness measurements inJ, H, andKs bands at DOME A with NISBM and early results

ABSTRACTThe radiance of sky brightness differs principally with wavelength passband. Atmospheric scattering of sunlight causes the radiation in the near-infrared band. The Antarctic is a singular area of the planet, marked by an unparalleled climate and geographical conditions, including the coldest temperatures and driest climate on Earth, which leads it to be the best candidate site for observing in infrared bands. At present, there are still no measurements of night-sky brightness at DOME A. We have developed the Near-Infrared Sky Brightness Monitor (NISBM) in the J, H, and Ks bands for measurements at DOME A. The instruments were installed at DOME A in 2019 and early results of NIR sky brightness from 2019 January–April have been obtained. The variation of sky background brightness with solar elevation and scanning angle is analysed. The zenith sky flux intensity for the early night at DOME A in the J band is in the 600–1100 μJy arcsec−2 range, that in the H band is between 1100 and 2600 μJy arcsec−2, and that in the Ks band is in the range ∼200–900 μJy arcsec−2. This result shows that the sky brightness in J and H bands is close to that of Ali in China and Mauna Kea in the USA. The sky brightness in the Ks band is much better than that in Ali, China and Mauna Kea, USA. This shows that, from our early results, DOME A is a good site for astronomical observation in the Ks band.

Lateral terrestrial water flow schemes for the Noah-MP land surface model on both natural and urban land surfaces

Soil moisture plays an important role in numerical weather forecasting and climate projection studies. Surface runoff is significant not only in soil moisture monitoring but also in flood forecasting. However, the current Noah land surface model with multi-parameterization options (Noah-MP) is nearly vertical and one-dimensional, and it does not consider surface water ponding and overland flow. To address this, lateral terrestrial water flow schemes has been developed for Noah-MP on both natural and urban land surfaces to improve the simulation of soil moisture. These schemes have enabled the conversion of Noah-MP to a three-dimensional land surface model (Noah-MP-3D). Water balance schemes for natural and urban surfaces have also been developed and coupled with Noah-MP-3D to improve water inundation simulation. The performance of Noah-MP and Noah-MP-3D has been compared and validated at two soil moisture observation sites. The results indicate that after using the terrestrial lateral flow schemes has significantly improved the simulations for 10 cm and 40 cm volumetric soil moisture.

Parameterization of soil evaporation and coupled transport of moisture and heat for arid and semiarid regions

Soil moisture is an important parameter in numerical weather forecasting and climate projection studies, and it is extremely important for arid and semiarid areas. Different from those in relatively wet areas, for arid and semiarid areas, mechanisms associated with the transportation, condensation, and evaporation of water vapor in soil interior cannot be neglected. In this paper, schemes associated with soil evaporation and coupled transport of soil moisture and heat were developed for the integrated urban land model (IUM) to improve the simulation of soil moisture in arid and semiarid areas. The whole layer soil evaporation (WSE) scheme was developed to improve the simulation of soil evaporation. The soil’s inner layer water vapor transport is considered a part of WSE. The transport of water and heat in the inner soil was linked to the phase change of water. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture product and 10-cm volumetric soil moisture observations in 358 autonomic soil moisture observation sites were used for validating the simulation results. The results indicate that after using the WSE scheme and considering the coupled transport of moisture and heat in the soil interior, the simulation of soil moisture was improved definitely. For June, July, and August, the biases of soil moisture simulation decreased by approximately 22.5, 34.4, and 27.5%, respectively, while the RMSEs decrease by approximately 7.0, 8.7, and 9.6%, respectively. The improvement of soil moisture simulation indicates that in arid and semiarid areas soil water vapor transport is important and cannot be neglected.

A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

ABSTRACT A high-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth products and reanalysis snow depth products. However, existing gridded snow depth products have some shortcomings. Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth, while reanalysis snow depth products have coarse spatial resolutions and great uncertainties. To overcome these problems, in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), the Northern Hemisphere Snow Depth (NHSD), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), incorporating geolocation (latitude and longitude), and topographic data (elevation), which were used as input independent variables. More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time periods. This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°. Here, we compared the fused snow depth and the original gridded snow depth products with 13,272 observation sites, showing an improved precision of our product. The evaluation indices of the fused (best original) dataset yielded a coefficient of determination R2 of 0.81 (0.23), Root Mean Squared Error (RMSE) of 7.69 (15.86) cm, and Mean Absolute Error (MAE) of 2.74 (6.14) cm. Most of the bias (88.31%) between the fused snow depth and in situ observations was in the range of −5 cm to 5 cm. The accuracy assessment of independent snow observation sites – Sodankylä (SOD), Old Aspen (OAS), Old Black Spruce (OBS), and Old Jack Pine (OJP) – showed that the fused snow depth dataset had high precision for snow depths of less than 100 cm with a relatively homogeneous surrounding environment. The results of random point selection and independent in situ site validation show that the accuracy of the fused snow depth product is not significantly improved in deep snow areas and areas with complex terrain. In the altitude range of 100 m to 2000 m, the fused snow depth had a higher precision, with R2 varying from 0.73 to 0.86. The fused snow depth had a decreasing trend based on the spatiotemporal analysis and Mann-Kendall trend test method. This fused snow depth product provides the basis for understanding the temporal and spatial characteristics of snow cover and their relation to climate change, hydrological and water cycle, water resource management, ecological environment, snow disaster and hazard prevention.

Microplastics Content of Seaweeds in the Mariculture Potential Zone at The Southwest of Coastal Bawean Island

Bawean Island is a sheltered and clean coastal waters that can be exploited for mariculture, particularly seaweed. Due to its enclosed environment, the presence of different wastes, particularly microplastics, is deemed negligible. The purpose of this research is to investigate the prevalence of microplastics in numerous seaweeds on Bawean Island's southwest shore, as well as the viability of seaweed production. At a depth of 1-6 meters, samples of red, green, and brown seaweed were obtained and examined for microplastics by destroying the cell walls. At the two observation sites, water characteristics such as current, pH, salinity, TDS, DO, substrate, temperature, depth, and brightness were also monitored. Based on the results, the southwest coastal waters of Bawean were suitable for seaweed cultivation. Furthermore, the lowest-highest microplastic concentration was discovered in Gracillaria sp. 44±18 particles/100g, Halimeda sp. 52±16 particles/100g, Caulerpa sp. 94±31 particles/100g, and Sargassum sp. 251±59 particles/100g, with a form of microplastic found in fragments, fibers, and films. Various colors of microplastics were found. According to this study, seaweed can act as a vector for microplastics to infiltrate the food chain. This is indicated by the substantial accumulation of microplastics in cultivation potential zones of seaweed where there are fewer human activities. This microplastic accumulation could then occur at a higher trophic level, thus seaweed in nature must be quarantined and cleaned after cultivation to reduce the concentration of microplastic contamination.

Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange

Abstract. Accurate estimates of net ecosystem CO2 exchange (NEE) would improve the understanding of natural carbon sources and sinks and their role in the regulation of global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting year-round 6 h NEE over 1996–2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve considerably the accuracy of both ML approaches compared to using only the nearest grid cell and time step. Both ML methods can explain temporal variability of NEE in the observational site of this study with meteorological predictors, but the GB method was more accurate. Only minor signs of overfitting could be detected for the GB algorithm when redundant variables were included. The accuracy of the approaches, measured mainly using cross-validated R2 score between the model result and the observed NEE, was high, reaching a best estimate value of 0.92 for GB and 0.88 for RF. In addition to the standard RF approach, we recommend using GB for modeling the CO2 fluxes of the ecosystems due to its potential for better performance.

Social and Economical effects of Custom Taxes and Methods of Collecting them in Iraq (334-590AH/945-1193AD)

Social and Economical effects of Custom Taxes and Methods of Collecting them in Iraq (334-590AH/945-1193AD) Prof. Muhannad Nafe'i Khattab Al-Mukhtar Mosul University / College of Arts / Department of History This research tries to study the economic and social effects produced that the customs collection produce in the Abbasid state which were imposed on the external trade then its change to tolls taxations imposed in ratio which additional thand the decided one and collected by arbitrary ways. This research has many objectives to achieves through studying the topic of research, then recognize them as the four section of the research which could be summarized as the following: 1- Are the commercial customs taxation ( the tithes )considered as the toll ? 2- The commercial customs taxations, When and how. 3- The road-blacks and the observation sites and the centers of collecting the trade taxation. 4- The economic social effects of the commercial customs taxation. The research adopted numerous methods besides the descriptive method to describe the historical telling's to invest them in the texts of the research, so, the inductive method has been used throughout reading the whole telling's that have to do with the topic of the research to conclude throughout it to some points that made the matter so clear to resolve the problems as well as the analytical and critical method played their role in this research.

Ground-Based FTIR-Measurements of the Atmospheric Nitric Acid at the NDACC Site of St. Petersburg

Atmospheric nitric acid (HNO3) has a significant impact on the formation of the ozone layer; therefore, its content is regularly monitored using various local and remote-sensing methods. We used ground-based measurements of solar IR spectra with a Bruker 125HR Fourier spectrometer to derive information on the HNO3 content at the St.Petersburg observational NDACC site in Peterhof. The HNO3 time series obtained showed a pronounced seasonal cycle with a maximum in winter and early spring and a minimum in summer and early autumn. The averaged seasonal variations in nitric acid varied from –30 to +60% for the 0–15 km layer, from –25 to +25% for the 15–50 km layer, and from –25 to +30% for total columns. For 2009–2022 measurement period, no statistically significant trend was found in the time series considered. Comparison of HNO3 stratospheric columns with independent satellite measurements by the MLS and ACE-FTS instruments showed their qualitative and quantitative agreement; the correlation coefficient between ground-based and satellite measurements totals 0.88–0.93. Time series on the vertical structure of the atmospheric nitric acid measured at the St.Petersburg site can be used both to analyze the state of the ozonosphere and to validate satellite measurements and refine the parameters of atmospheric models.

Effects of Land-Use and Environmental Factors on Snail Distribution and Trematode Infection in Ethiopia

Freshwater snails are intermediate hosts for several snail-borne diseases affecting humans and animals. Understanding the distribution of snail intermediate hosts and their infection status is very important to plan and implement effective disease prevention and control interventions. In this study, we determined the abundance, distribution, and trematode infection status of freshwater snails in two agro-ecological zones of Ethiopia. We sampled snails from 13 observation sites and examined them for trematode infections using a natural cercarial shedding method. A redundancy analysis (RDA) was used to examine the relationship between snail abundance and environmental variables. Overall, a total of 615 snails belonging to three species were identified. Lymnea natalensis and Bulinus globosus were the dominant snail species, representing 41% and 40% of the total collection, respectively. About one-third of the total snail population (33%) shed cercariae. The cercariae species recorded were Xiphidiocercaria, Brevifurcate apharyngeate distome (BAD), Echinostome, and Fasciola. Snail species were found in high abundance in aquatic habitats located in the agricultural landscape. Therefore, land-use planning and protection of aquatic habitats from uncontrolled human activities and pollution can be considered as important strategies to prevent and control the spread of snail-borne diseases in the region.

Investigation of HO2 uptake mechanisms onto multiple-component ambient aerosols collected in summer and winter time in Yokohama, Japan

The heterogeneous loss of HO2 radicals onto ambient aerosols plays an important role in tropospheric chemistry. However, sparse investigation of the dominating parameters controlling the HO2 uptake coefficients onto ambient aerosols (γHO2) has largely hindered the application of the measured γHO2 to the global spatial prediction. Here we induced an offline method using LFP-LIF technique to measure the kinetics of HO2 uptake onto ambient aerosols collected in summertime and wintertime in Yokohama city, a regional urban site near Tokyo, Japan. By controlling the dominating parameters which influence γHO2, we were able to investigate the detailed HO2 uptake mechanism. We characterized the chemical composition of the collected ambient aerosols, including organics, inorganics, transition metals ions, etc. and modeled γHO2 using different mechanisms. Results show that γHO2 increased with the increase in RH, and the aerosol states (“dry” or wet/aqueous) have large effects on γHO2. With fixed RH and aerosol chemical composition, γHO2was highly dependent on pH and inversely correlated with [HO2]0. By combing the measured γHO2 values with the modeled ones, we found that both the HO2 self-reaction and transition metal-catalyzed reactions should be accounted for to yield a single parameterization to predict γHO2, and different chemical compositions may have collective effects on γHO2. Results may serve for extending the γHO2 values measured at one observation site to different environmental conditions, which will help us to achieve more accurate modeling results concerning secondary pollutant formation (i.e., ozone).

Dataset of Comparative Observations for Land Surface Processes over the Semi-Arid Alpine Grassland against Alpine Lakes in the Source Region of the Yellow River

Thousands of lakes on the Tibetan Plateau (TP) play a critical role in the regional water cycle, weather, and climate. In recent years, the areas of TP lakes underwent drastic changes and have become a research hotspot. However, the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear, which inhibits model development and the accurate simulation of lake climate effects. The source region of the Yellow River (SRYR) has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP. Since 2011, three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces. This study presents an eight-year (2012–19), half-hourly, observation-based dataset related to lake–atmosphere interactions composed of three sites. The three sites represent the lake surface, the lakeside, and the land. The observations contain the basic meteorological elements, surface radiation, eddy covariance system, soil temperature, and moisture (for land). Information related to the sites and instruments, the continuity and completeness of data, and the differences among the observational results at different sites are described in this study. These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model. The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.