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This paper focuses on the atmospheric dust transport effect on the changes in chlorophyll-A concentration in the Black Sea surface layer. In order to assess the input of nutrients with atmospheric precipitations at the Crimean coast of the Black Sea, the collected samples were analyzed for the content of inorganic nitrogen, phosphates, and silicon. The samples were taken into a wet-only sampler and into a permanently open one, to assess the effect of dust on the nutrients concentration in dry depositions. Cases of multi-fold excess of the nutrients content in the open sampler collected precipitation over that in the wet-only sampler were identified. For such high concentration cases, the 7-day back-trajectories analyses was carried out using the model of the international network AERONET and the HYSPLIT model. The results of our research showed that the influx of nutrients with the atmospheric depositions can result in increasing of chlorophyll-A concentration in 11–36% in the surface layer of the Black Sea. After atmospheric depositions, concentration of phosphates in the surface layer can increase more than five times compared with the background concentration. The increase of silicon concentration can reach 30%. The influx of atmospheric precipitation containing significant amounts of nutrients into the bay can shifts the Redfield ratio compared with background value up to three times. [ABSTRACT FROM AUTHOR]

Extreme rainstorms have important socioeconomic consequences, but understanding their fine spatial structures and temporal evolution still remains challenging. In order to achieve this, in view of an evolutionary property of rainstorms, this paper designs a process-oriented algorithm for identifying and tracking rainstorms, named PoAIR. PoAIR uses time-series of raster datasets and consists of three steps. The first step combines an accumulated rainfall time-series and spatial connectivity to identify rainstorm objects at each time snapshot. Secondly, PoAIR adopts the geometrical features of eccentricity, rectangularity, roundness, and shape index, as well as the thematic feature of the mean rainstorm intensity, to match the same rainstorm objects in successive snapshots, and then tracks the same rainstorm objects during a rainstorm evolution sequence. In the third step, an evolutionary property of a rainstorm sequence is used to extrapolate its spatial location and geometrical features at the next time snapshot and reconstructs a rainstorm process by linking rainstorm sequences with an area-overlapping threshold. Experiments on simulated datasets demonstrate that PoAIR performs better than the Thunderstorm Identification, Tracking, Analysis and Nowcasting algorithm (TITAN) in both rainfall tracking and identifying the splitting, merging, and merging-splitting of rainstorm objects. Additionally, applications of PoAIR to Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM/IMERG) final products covering mainland China show that PoAIR can effectively track rainstorm objects. [ABSTRACT FROM AUTHOR]

This research used the geostatistical analysis of snow cover samples taken in 2017–2022 in the Baikal basin. Groups of snow cover pollution sources were identified by the method of empirical Bayesian kriging (ArcMap software) and mathematical data processing. The studied area was divided into fourteen districts. Geovisualization of marker substances accumulated in the snow cover allowed for the zoning of the studied area according to the degree of anthropogenic load. It was revealed that the atmospheric pollution of the territory from local sources extended for tens of kilometers along the prevailing wind direction. The maximum concentrations of anthropogenic aerosols in the snow cover were determined in towns that were sources of pollution and near settlements located on the coast of Lake Baikal and at the Selenga River mouth. The industrial centers of the region and the southern basin of Baikal, being affected by the air emissions from the Irkutsk agglomeration, were determined to be the most susceptible to anthropogenic pollution. The middle and northern basins could be attributed to the background regions being affected only by local heating sources and the natural background. The main atmospheric pollutants and the areas of their distribution were established. The main sources of snow cover pollution in the region, in addition to the natural background, were emissions from thermal power plants and motor vehicles. [ABSTRACT FROM AUTHOR]

The coastal zone area of Qionghai City is one of the important coastal zones in the South China Sea, and its water environment has been affected by human activities such as urbanization and industrialization. In order to protect the water resources and ecological environment of this area, the water chemistry characteristics of the main watersheds and their causes in the coastal zone area of eastern Hainan Island were investigated to provide a scientific basis for environmental protection and sustainable development. In this study, the characteristics and sources of water chemical ion components were analyzed using a Piper trilinear diagram, Gibbs diagram, and correlation analysis with the coastal zone area of Qionghai city as the research object. The results show the following: (1) the dominant cation of water chemistry in the coastal zone of Qionghai City is Na+ with a mean value of 35.001 mg·L−1, and the dominant anion is Cl− with a mean value of 30.69 mg·L−1; (2) the dominant cation content in the coastal zone of Qionghai City is Na+ > Ca2+ > Mg2+ > K+, and the dominant anion content is Cl− > SO42− > HCO3− > CO32−; (3) at the five collection sites in the study area, the ion concentrations showed different trends, with the highest ion concentration in the water samples collected from aquaculture ponds, and the main water chemistry type was Na-Cl; the lowest ion concentration was in the water samples collected from the rivers, and the main type of water chemistry was Ca·Mg-HCO3. The source of water chemistry ions in the study area mainly included seawater, rock weathering, atmospheric precipitation, and evaporation concentration. The results of this study can provide a scientific basis for the development, utilization, and management of local water resources and provide basic data for environmental protection and sustainable development. [ABSTRACT FROM AUTHOR]

To address the problem of the collapse of the roof of the Bailuyuan tunnel during construction, the causes of collapse were analyzed, targeted treatment measures were proposed, and the effects of the treatment measures were evaluated through on-site monitoring and three-dimensional numerical simulations. The results showed that the particular characteristics of loess and the synergy of groundwater were the internal causes of the tunnel's collapse as well as, to a certain extent, atmospheric precipitation. Therefore, the combination of multiple factors contributed to the tunnel's collapse. Untimely monitoring and measurement, as well as the low initial support parameters, reflect a lack of human understanding of the collapse. Based on the analysis of the causes of the collapse, comprehensive treatment measures for inside and outside the tunnel are proposed, which are shown to be effective and to be capable of preventing the occurrence of further collapses. After the collapse treatment, the measured maximum settlement of the tunnel vault was 65.1 mm, the maximum horizontal convergence was 25 mm, the maximum surrounding rock pressure was 0.56 MPa, and the maximum stress on the steel arch frame was 54.34 MPa. Compared with the original design plan, the vertical stress, horizontal stress, and shear stress of the surrounding rock obtained from numerical simulation after the collapse treatment were greatly reduced, the reduction rate at the vault reached 50%, and the safety factors of the initial support positions after treatment met the specification requirements. The research results can provide engineering guidance for the design and construction of large-section tunnels crossing deep-loess strata, and they are of important engineering significance. [ABSTRACT FROM AUTHOR]

Microbial-induced calcite precipitation (MICP) is a soil amelioration technique aiming to mitigate different environmental and engineering concerns, including desertification, soil erosion, and soil liquefaction, among others. The hydrolysis of urea, catalyzed by the microbial enzyme urease, is considered the most efficient microbial pathway for MICP. Biostimulated MICP relies on the enhancement of indigenous urea-hydrolyzing bacteria by providing an appropriate enrichment and precipitation medium, as opposed to bioaugmentation, which requires introducing large volumes of exogenous bacterial cultures into the treated soil along with a growth and precipitation medium. Biostimulated MICP in desert soils is challenging as the total carbon content and the bacterial abundance are considerably low. In this study, we examined the biostimulation potential in soils from the Negev Desert, Israel, for the purpose of mitigation of topsoil erosion in arid environments. Incubating soil samples in urea and enrichment media demonstrated effective urea hydrolysis leading to pH increase, which is necessary for calcite precipitation. Biostimulation rates were found to increase with concentrations of energy (carbon) source in the stimulation media, reaching its maximal levels within 3 to 6 days. Following stimulation, calcium carbonate precipitation was induced by spiking stimulated bacteria in precipitation (CaCl2 enriched) media. The results of our research demonstrate that biostimulated MICP is feasible in the low-carbon, mineral soils of the northern Negev Desert in Israel. [ABSTRACT FROM AUTHOR]

Hollow Co3O4 nanoboxes constructed by numerous nanoparticles were prepared by using a facile method consisting of precipitation, solvothermal and annealing reactions. The desirable hollow structure as well as a highly porous morphology led to synergistically determined and enhanced supercapacitor performances. In particular, the hollow Co3O4 nanoboxes were comprehensively investigated to achieve further optimization by tuning the sizes of the nanoboxes, which were well controlled by initial precipitation reaction. The systematical electrochemical measurements show that the optimized Co3O4 electrode delivers large specific capacitances of 1832.7 and 1324.5 F/g at current densities of 1 and 20 A/g, and only 14.1% capacitance decay after 5000 cycles. The tunable synthesis paves a new pathway to get the utmost out of Co3O4 with a hollow architecture for supercapacitors application. [ABSTRACT FROM AUTHOR]

The present research was carried out to study drought and its effects upon water resources using remote sensing data. To this end, the tropical rainfall measuring mission (TRMM) satellite precipitation, the synoptic stations, and fountain discharge data were employed. For monitoring of drought in the study area, in Kermanshah province, Iran, the monthly precipitation data of the synoptic stations along with TRMM satellite precipitation datasets were collected and processed in the geographic information system (GIS) environment. Statistical indicators were applied to evaluate the accuracy of TRMM precipitation against the meteorological stations' data. Standardized precipitation index, SPI, and normalized fountain discharge were used in the monitoring of drought conditions, and fountains discharge, respectively. The fountains were selected so that in addition to enjoying the most discharge rates, they spread along the study area. The evaluation of precipitation data showed that the TRMM precipitation data were of high accuracy. Studies in temporal scale are indicative of the strike of drought in this region to the effect that for most months of the year, frequency and duration in dry periods are much more than in wet periods. As for seasonal scales, apart from winter, the frequency and duration of drought in spring and autumn have been longer than in wet years. Moreover, the duration of these periods was different. A comparison between the results of changes in fountain discharges and drought index in the region has verified that the drought has caused a remarkable decline in the fountain discharges. [ABSTRACT FROM AUTHOR]

Featured Application: The following research has a potential to provide a scientific basis for the management of drought mitigation strategies. The trends in drought characteristics in each zone throughout the study period can help decision-makers with more information on resource planning. The identification of contiguous zones with similar drought characteristics across Pakistan is the first step towards developing an integrated and holistic policy to minimize the impacts of droughts. This study will help achieve this goal. Pakistan is among the top ten countries adversely affected by climate change. More specifically, there is concern that climate change may cause longer and severer spells of droughts. To quantify the change in the characteristics of droughts in Pakistan over the years, we have evaluated spatio-temporal trends of droughts in Pakistan over the period 1902–2015 using Standardized Precipitation Evapotranspiration Index (SPEI). Additionally, the Spatial "K" luster Analysis using Tree Edge Removal (SKATER) method was employed to regionalize droughts into five contiguous zones. The run theory was then applied to each zone to identify drought events and characterize them in terms of duration, severity, intensity, and peak. Moreover, the Modified Mann–Kendall trend test was applied to identify statistically significant trends in SPEI and drought characteristics in each zone. It was found that the southern areas of Pakistan, encompassing Sindh and most of Baluchistan, have experienced a decrease in SPEI, indicating a drying trend. Central Pakistan has witnessed a wetting trend as demonstrated by an increase in SPEI over time, whereas no statistically significant trend was observed for the northern areas of Pakistan. On a zonal basis, the longest duration drought to occur in Pakistan lasted 22 months in zone 5 (Sindh) from 1968 to 1970. In addition, the droughts of 1920 and 2000 can be said to be the worst drought in the history of the region as it affected all the zones and lasted for more than 10-months in three zones. [ABSTRACT FROM AUTHOR]

Featured Application: A conceptual model of aerosol-cloud interaction is first built to explain the aerosol effect on Mei-Yu frontal precipitation, with possible application to reducing the significant uncertainties in weather and climate prediction. The Mei-Yu front is a significantly important summer precipitation system in eastern Asia. In recent years, anthropogenic air pollution over the Yangtze-Huaihe region of China has been aggravating continuously. A cloud-resolving model coupled with an idealized frontal model is used to investigate the response of aerosols on the Mei-Yu frontal precipitation. The results indicate that increasing droplet concentrations lead to significant precipitation enhancement with the current pollution levels in Mei-Yu frontal system. Under the polluted conditions, the enhanced cold-cloud process is of great importance. Moreover, with the "towing" of active cold-cloud process, cold-cloud and warm-cloud processes developed mutually. These account for the complicated and special microphysical mechanism for aerosol impacts on Mei-Yu frontal system. Furthermore, two types of "microphysical-dynamic positive feedback loop" caused by the interactions of various physical processes and effects (direct dynamic effect, frontogenesis effect, and vapor pump effect) can be found in the Mei-Yu precipitation, which in turn reinforce the microphysical processes. The combined effect is to increase Mei-Yu front precipitation. The interaction of microphysical processes and dynamic processes, and the positive feedback loops they create are the main physical mechanisms behind the significant impacts of aerosol on Mei-Yu frontal precipitation. This may also be an important feature of climate change in eastern Asia. [ABSTRACT FROM AUTHOR]

Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data. [ABSTRACT FROM AUTHOR]