Significant Seasonal Differences Research Articles

Analysis of Long‐Term Trends in the Vertical Distribution and Transport Paths of Atmospheric Aerosols in Typical Regions of China Using 15 Years of CALIOP Data

AbstractLong‐range transport and vertical distribution of aerosols are important factors for assessing the uncertainty in aerosol radiative forcing. This paper reveals the vertical distribution and trends of aerosol optical properties in China using 15 years of Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The Hybrid Single‐Particle Lagrangian integrated trajectory model was used to analyze the transport and trends of aerosols in the layer with the highest occurrence frequencies of dust, polluted dust, polluted continental and elevated smoke aerosols. The results indicated that (a) there were significant regional and seasonal differences in the vertical distribution of aerosols. The aerosol optical depth (AOD) trend in a given region depends on the changes in the aerosol type with the highest frequency and the layer corresponding to the largest AOD in the vertical profile. The frequency of polluted dust aerosols was the highest in Beijing‐Tianjin‐Hebei (BTH) and Central China. The considerable decrease in the 0–2 km AOD led to a significant trend of the column AOD. (b) The changes of AOD and main aerosol types in a region are also affected by the changes of aerosol sources and long‐range transport pathways. In the BTH, dust aerosols originated from the Mongolian Plateau, accounting for 57.88% of the total trajectories. The Pearl River Delta was dominated by elevated smoke aerosols, with trajectories mainly originating from the Myanmar and Vietnam, accounting for 27.38% and 29.59%, respectively. The trend of 15‐year backward trajectories of dust aerosols on the Tibetan Plateau indicated that the trajectory from India is increasing.

Atmospheric Ozone Concentration Prediction in Nanjing Based on LightGBM

Based on the air quality data and conventional meteorological data of the Nanjing Region from January 2015 to December 2016, to analyze the characteristics of O3 concentration changes in the Nanjing Region, a light gradient boosting machine (LightGBM) model was established to predict O3 concentration. The model was compared with three machine learning methods that are commonly used in air quality prediction, including support vector machine, recurrent neural network, and random forest methods, to verify its effectiveness and feasibility. Finally, the performance of the prediction model was analyzed under different meteorological conditions. The results showed that the variation in O3 concentration in Nanjing had significant seasonal differences and was affected by a combination of its pre-concentration, meteorological factors, and other air pollutant concentrations. The LightGBM model predicted the ground-level O3 concentration in the Nanjing area more precisely to a large extent (R2=0.92), and the model outperformed other models in prediction accuracy and computational efficiency. In particular, the model showed a significantly higher prediction accuracy and stability than that of other models under a high-temperature condition that was more likely prone to ozone pollution. The LightGBM model was characterized by its high prediction accuracy, good stability, satisfactory generalization ability, and short operation time, which broaden its application prospect in O3 concentration prediction.

A year-round observation of δ13C of dicarboxylic acids and related compounds in fine aerosols: Implications from Central European background site

Isotopic analysis of specific compounds in aerosols can be a useful tool when studying atmospheric processes. Here, we present the results of stable carbon isotope ratio (δ13C) measurements performed on a one-year set (n = 96, Sep. 2013–Aug. 2014) of dicarboxylic acids and related compounds in PM1 at a rural Central European background site, Košetice (Czech Republic).The most 13C enriched acid was oxalic (C2, annual average = −16.6 ± 5.0‰) followed by malonic (C3, avg. = −19.9 ± 6.6‰) and succinic (C4, avg. = −21.3 ± 4.6‰) acids. Thus, δ13C values decreased with an increase in carbon numbers. Azelaic acid (C9, avg. = −27.2 ± 3.6‰) was found to be the least 13C enriched.A comparison of δ13C of dicarboxylic acids from other background sites, especially in Asia, shows similar values to those from the European site. This comparison also showed that C2 is more 13C enriched at background sites than at urban ones. In general, we did not observe significant seasonal differences in δ13C values of dicarboxylic acids at the Central European station. We observed statistically significant differences (p value < 0.05) between winter and summer δ13C values solely for C4, glyoxylic acid (ωC2), glutaric acid (C5) and suberic acid (C8).The only significant correlations between δ13C of C2 and δ13C of C3 were found in spring and summer, suggesting that the oxidation of C3 to C2 is significant in these months with a strong contribution from biogenic aerosols. The strongest season-independent annual correlation was observed in δ13C values between C2 and C4, the two dominant dicarboxylic acids. Therefore, C4 appears to be the main intermediate precursor of C2 throughout the whole year.

Temperature and phosphorus: the main environmental factors affecting the seasonal variation of soil bacterial diversity in Nansi Lake Wetland.

The soil bacteria promote the circulation conversion of lake nutrients and play an important role in maintaining the balance of the lake ecosystem. Few studies have investigated the association of seasonal variation in bacteria and environmental factors in inland freshwater lake wetlands. Nansi Lake is a large shallow freshwater lake in northern China. It is an important hub of the eastern route of the South-to-North Water Diversion Project. In this study, bacterial 16S rRNA genes were used to analyze the variation of soil bacterial community diversity in Nansi Lake Wetland and its influencing factors in different seasons. It is showed that the phylum, family, and genus with the largest relative abundance in the soil of Nansi Lake Wetland are Proteobacteria, Nitrosomonadaceae, and MND1, respectively. There were significant seasonal differences in soil bacterial diversity in Nansi Lake Wetland, which was significantly higher in summer than in winter. Seasonal variation in environmental factors was significantly correlated with the variation in bacterial communities. Temperature and the content of available phosphorus may be the key factors influencing seasonal variation in bacterial diversity. The results of this study further enhance our understanding of the relationship between bacterial community diversity and environmental factors in the lake wetland ecosystem, which can provide scientific data for the conservation of Nansi Lake Wetland.

Characterizing legacy nitrogen-induced time lags in riverine nitrogen reduction for the Songhuajiang River Basin: Source analysis, spatio-seasonal patterns, and impacts on future water quality improvement

Legacy nitrogen (N) originating from net N inputs (NNI) may pose ongoing threats to riverine water quality worldwide and even cause serious time-lags between water quality restoration and NNI declines. A better understanding of legacy N effects on riverine N pollutions in different seasons is essential to improve riverine water quality. Here, we investigated contributions of legacy N on riverine dissolved inorganic N (DIN) changes in different seasons and quantified spatio-seasonal time-lags in the Songhuajiang River basin (SRB), a hotspot of NNI with four distinct seasons, by exploring long-term (1978-2020) NNI-DIN relationships. Results firstly showed a significant seasonal difference in NNI, with the highest value observed in spring (average, 2184.1 kg/km2), 1.2, 5.0, and 4.6 times higher than that in summer, autumn, and winter, respectively. Cumulative legacy N had dominated riverine DIN changes, with a relative contribution of approximately 64% in 2011–2020, causing time-lags of 11–29 years across the SRB. The longest seasonal lags existed in spring (average, 23 years) owing to greater impacts of legacy N to riverine DIN changes in this season. Mulch film application, soil organic matter accumulation, N inputs, and snow cover were identified as the key factors that strengthened seasonal time-lags by collaboratively enhancing legacy N retentions in soils. Furthermore, a machine learning-based model system suggested that timescales for water quality improvement (DIN, ≤1.5 mg/L) varied considerably (from 0 to >29 years, Improved N Management-Combined scenario) across the SRB, with greater lag effects contributing to slower recovery. These findings can provide a more comprehensive insight into sustainable basin N management in the future.

Seasonal expressions of COX-1, COX-2 and EP4 in the scent glands of muskrats (Ondatra zibethicus).

Prostaglandins (PGs) serve as signaling molecules that regulate various physiological processes, including inflammation, immune response, blood clotting and reproduction. The aim of this study was to investigate the immunolocalizations and expression patterns of prostaglandin-E2 (PGE2), cyclooxygenase (COX)-1 and COX-2, as well as its receptor subtypes 4 (EP4) in the scent glands of muskrats (Ondatra zibethicus) during the breeding and non-breeding periods. There were significant seasonal differences in the scent glandular mass, with higher values in the breeding season and relatively low in the non-breeding season. PGE2, EP4, COX-1 and COX-2 have been immunolocalized in the scent glandular and epithelial cells in both breeding and non-breeding seasons, whereas no immunostaining was observed in the interstitial cells. The protein and mRNA expression levels of EP4, COX-1 and COX-2 were higher in the scent glands of the breeding season than those of the non-breeding season. The mean mRNA levels of EP4, COX-1 and COX-2 were positively correlated with the scent glandular weights. The circulating follicle stimulating hormone (FSH), luteinizing hormone (LH), testosterone (T) and PGE2, as well as scent glandular PGE2 and dihydrotestosterone (DHT) concentrations were also significantly higher in the breeding season. Additionally, the transcriptomic study in the scent glands identified that differentially expressed genes might be related to fatty carboxylic monocarboxylic acid, steroidogenic-related pathway and prostanoid metabolic process. These findings suggested that prostaglandin-E2 might play an essential autocrine or paracrine role in regulating seasonal changes in the scent glandular functions of the muskrats.

Comigration and interactions between two species of rice planthopper (Laodelphax striatellus and Sogatella furcifera) and natural enemies in eastern Asia.

Natural enemies are important in pest control. However, control by natural enemies is hindered by the migration of rice planthoppers. Therefore, comigration and interactions between Laodelphax striatellus (Fallén) and Sogatella furcifera (Horváth) and five predator species, Chrysoperla sinica Tjeder, Harmonia axyridis (Pallas), Episyrphus balteatus, Syrphus corollae (Fab.) and Chrysopa pallens (Rambur) in eastern Asia were investigated. From 2012 to 2021, the migration patterns of two rice planthoppers and five natural enemy species were monitored by suction trapping on Beihuang Island, Shandong Province, China. Both planthoppers and the five natural enemies regularly comigrated from late April to late October each year. There were significant interannual and seasonal differences in the numbers of two rice planthoppers migrating across this island. Simulated seasonal migration trajectories indicated different source areas for the two rice planthoppers, which mainly originated in northeast, north and east China. The biomass of planthoppers was significantly positively correlated with that of the ladybug H. axyridis in all migration periods, and significant differences in the ratio of rice planthoppers to natural enemies among months. A time-lag effect between seasons was obtained when natural enemies and pests comigrated. Migration was coordinated between rice planthoppers and natural enemies in East Asia. When natural enemies and rice planthoppers comigrated, time lags between seasons were observed. The unique insights into the migration patterns will help to increase understanding of the occurrence of rice planthoppers in eastern Asia and provide an important theoretical basis for regional monitoring and management of rice planthoppers. © 2023 Society of Chemical Industry.

Analysis of Water Quality and Habitat Suitability for Benthic Macro-Invertebrates in the Majiagou Urban River, China

The macro-invertebrate is an important part of the aquatic food web of urban rivers, and it is of great significance in understanding its ecological suitability for the stability of river ecosystems. Previous studies, such as those that have conducted suitability index and canonical correspondence analyses (CCAs), have widely used a macro-invertebrate suitability analysis; however, these studies can only confirm a few coupling relationships between the environment and macro-invertebrates. In our study, one-way ANOVA, HCA, PCA and GAM models were used to explain the differences in the spatial and temporal distribution of environmental factors, as well as to reduce data redundancy. A response curve of the critical environmental factors and macro-invertebrates was constructed, and the nonlinear relationship between these factors and benthic animals was quantified to analyze the ecological threshold of the macro-invertebrates. The study area was the Majiagou River, Harbin, China. The results show that COD had significant seasonal differences due to complex hydrological conditions, and most of the water quality factors had spatial differences. The GAM model explained 60% of the Margalef diversity index (MDI) variance. The relationship between chlorophyll-a and MDI was unimodal, and MDI and NH4+-N essentially showed a negative correlation; when the total nitrogen (TN) value reached 5.8 mg/L, MDI reached its peak. When MDI was higher than the mean value, the chlorophyll-a range was 18.1 μg/L~83 μg/L. The NH4+-N was less than 1.8 mg/L, and TN was 1.8~6.8 mg/L. This study provides a reference for the comprehensive management of urban river ecosystems.

Atmospheric wet deposition of trace metal elements: Monitoring and modelling

Trace elements (TEs), a group of atmospheric pollutants, have attracted considerable attention from scientists and government administrators worldwide. The wet deposition fluxes of nineteen trace elements (NTE) were monitored at Wanqingsha, a coastal site in the Pearl River Delta, for three consecutive years (2016.9–2019.8). Significant seasonal differences in NTE between wet and dry seasons were observed. The fluxes of crustal elements (Ca, Na, Al, Mg, K, Fe, Zn and Ba) were significantly higher than those of anthropogenic elements, accounting for over 99 % of the total annual wet deposition of 19 elements. Analysis of PM2.5 and rainfall samples reveals that both the fraction of each TE in the PM2.5 (CQ) and the Apparent Scavengance Ratio for TE (ASR, defined as the concentration ratio in rain and PM2.5) follow lognormal distributions. The logCQ variation for each element is relatively small but shows substantial differences, with means ranging from −5.48 to −2.03, while the logASRs for all elements show similar means (varying from 5.86 to 7.64) and an extremely wide range of variation. The influences of meteorological factors on CQ and ASR were also investigated. A simple box model framework was constructed to reasonably simplify the TE removal process by precipitation. The corresponding regression analysis showed significant correlations between NTE and the precipitation rate, PM2.5 concentration, ASR, and CQ, with R2 ranging from 0.711 to 0.970. By substituting the effects of environmental factors on ASR and CQ into the above relationship, temporal variations in NTE can be predicted. The reliability of the model was demonstrated by comparing model simulations with observations over three years. For most elements, the models can predict the temporal variation of NTE quite accurately, and even for the worst predictions, such as Al, Mg, K, Co and Cd, where predictions exceed observations by only an order of magnitude.

Identifying opportunities for harmonized microplastics and mesoplastics monitoring for Caribbean Small Island Developing States using a spatiotemporal assessment of beaches in South Eleuthera, The Bahamas

Increasing quantities of microplastics and mesoplastics in the marine environment underscore the need for marine microplastics to be included in the global Plastics Treaty to end plastic pollution. Caribbean Small Island Developing States (SIDS) lack harmonized microplastics monitoring protocols, leaving them data deficient at the science-policy interface required for treaty negotiations. This baseline study assessed spatial and seasonal abundance and distribution of microplastic (1–5 mm) and mesoplastic (5–25 mm) on 16 beaches with three coastal exposures (Atlantic Ocean, Exuma Sound, Bahama Bank) in South Eleuthera, The Bahamas and its implications for Caribbean SIDS. Microplastics were the dominant debris type sampled (74 %) across all beaches, with significant spatial (p = 0.0005) and seasonal (p = 0.0363) differences in abundance and distribution across study sites. This baseline study identifies opportunities required for developing harmonized microplastics and mesoplastics monitoring by Caribbean SIDS to collect data to help support global plastics treaty negotiations.

Source identification of trace elements in groundwater combining APCS-MLR with geographical detector

Accurately identifying the sources of trace elements in groundwater is difficult, especially in the areas with intensive human activities. This study proposes a framework to identify and quantify the contributions of potential sources of trace elements in groundwater. Health risk assessment is first conducted to identify the trace elements with the highest threats to groundwater quality, and then the geographical detector is used to qualitatively investigate the potential sources and processes dominating the pollution from identified trace elements, and the absolute principal component scores-multivariate linear regression (APCS-MLR) is used to quantify the contributions of different sources to the accumulation of trace elements. The proposed approach was applied to China’s Loess Plateau, a typical water-limited area with rapid urbanization and intensive agricultural activities, after conducting two groundwater sampling campaigns in a flood and a dry season. The trace element concentrations in groundwater exhibited significant spatial and seasonal differences. Health risk assessment revealed that Cr and As through oral ingestion were responsible for non-carcinogenic and carcinogenic risks in different seasons. Land use was the primary factor influencing the spatial heterogeneity of Cr in the flood season, while elevation and distance from city were dominant factors in the dry season. Normalized difference vegetation index (NDVI) and distance from city contributed the most to As change in the flood season, but NDVI was the major contributor in the dry season. The average contributions of potential pollution sources to trace elements in groundwater were in the order of natural source (35%) > geogenic process (17%) > industrial source (13%) in the flood season, but mixed sources (47%) > agricultural activities (18%) in the dry season. Distinguished from the traditional source apportionment methods, the newly proposed approach is not limited to identification of pollutant sources, but also can obtain the exact contributions of different sources. The findings provide useful information for groundwater management in the Loess Plateau.

Temporal and Spatial Variation Characteristics and Influencing Factors of Bacterial Community in Urban Landscape Lakes.

Urban landscape lakes are closely related to human activity, but there are limited studies on their bacterial community characteristics and risks to human health. In this study, four different types of urban landscape lakes in Xi'an were selected, and the bacterial community structures in different seasons were analyzed by Illumina Nova high-throughput sequencing technology. Seasonal variations in bacterial communities were analyzed by linear discriminant analysis, STAMP difference analysis, and nonmetric multidimensional scaling. Redundancy analysis was used to investigate the influencing factors. Furthermore, the metabolic functions of bacterial communities were predicted by Tax4Fun. There were clear seasonal differences in the α-diversity of bacteria, with bacterial diversity being higher in winter than in summer in the four urban landscape lakes, and the diversity of different water sources was different; the distributions of Proteobacteria, Actinobacteria, Chloroflexi, and Verrucomicrobia had significant seasonal differences; and the dominant bacteria at the genus level had obvious temporal and spatial differences. Furthermore, a variety of environmental factors had an impact on bacterial communities, and temperature, DO, and nitrogen were the primary factors affecting the seasonal variation in bacteria. There are also significant seasonal differences in the metabolic functions of bacterial communities. These results are helpful for understanding the current status of bacteria in the aquatic environments of such urban landscape lakes.

Mid-latitude spread F over an Extended European area

A three-year study (2017, 2020, 2021) of diurnal and seasonal characteristics of nighttime spread F over eight Digisonde European stations covering an extended latitude and longitude range of the European mid-latitude ionosphere is presented. The findings of the study underline the significant seasonal differences between higher and lower latitude stations which is in line with the findings of previous studies. Moreover, we attempt to correlate spread F activity and Travelling Ionospheric Disturbances (TID) over Digisondes, identified on GPS detrended TEC maps from the DRAWING-TEC project at 10-min resolution for 2017. The similarity in spread F activity at nearby stations supports the hypothesis that spread F development over nearby stations can be attributed to common TID activity affecting both stations.

Microplastic emission trends in Turkish primary and secondary municipal wastewater treatment plant effluents discharged into the Sea of Marmara and Black Sea

Wastewater Treatment Plants (WWTPs) are recognized as one of the primary sources of microplastics, a class of contaminants that has lately gained attention. The quantity of MP that WWTPs release into the environment depends on several factors, including the treatment type, season, and population serviced. MP abundance and characterization were explored in 15 WWTP effluent waters, 9 discharged to the Black Sea from Türkiye and 6 to the Marmara Sea, with varying population densities and treatment methods. The mean MP abundance in primary treatment WWTPs (76.25 ± 49.20 MP L−1) was found to be substantially greater than that in secondary treatment WWTPs (20.57 ± 21.56 MP L−1) (p<0.05). MPs in WWTP effluent waters showed significant seasonal and spatial differences (Two Way ANOVA, Tukey, p<0.05). However, no positive correlation was detected between the population serviced and MP abundance in effluent waters. While the fiber was the dominant shape (49.5%) among MPs in effluent waters, ≈80% of the length was <1000 μm. MPs are classified into polymer types as follows: polyethylene terephthalate (34.9%) > polypropylene (32.4%) > polyethylene (19.9%) > polyamide (11%) > polystyrene (1.2%) > polyvinyl chloride (0.6%). With effluent waters from the WWTPs tested, we calculated that 1.24x1010 daily MPs are discharged into the Black Sea while 4.95x1010 MPs are into the Marmara Sea, for a combined annual discharge of 2.26x1013 MPs highlighting that WWTPs are key contributors of MP in Turkish coastal waters.

Indoor air quality in Hungarian Passive Houses

The indoor air quality (IAQ) was investigated in 15 Passive Houses in the heating and non-heating seasons between 2019 and 2021 in Hungary. The concentrations of volatile organic compounds (VOCs), aldehydes, ozone, nitrogen dioxide, PM2.5 mass, carbon dioxide, bacteria, fungi, and pollen were measured together with the monitoring of temperature, relative humidity and air change rate (ACR). The IAQ varied considerably among the investigated buildings. Significant seasonal differences were obtained for all physical parameters (temperature, relative humidity, ACR), certain VOCs (benzene, α-pinene, limonene), acetaldehyde, and airborne fungi. Considerable health concern was associated with the indoor concentrations of PM2.5 mass and nitrogen dioxide in many cases based on the evaluation of IAQ in relation to potential adverse health effects, while the peak concentrations of other pollutants (e.g. trichloroethylene, α-pinene, certain aldehydes, fungi) were also of concern in a couple of cases. The median indoor/outdoor concentration ratios of benzene, PM2.5 mass, nitrogen dioxide, ozone and fungi indicated that these pollutants are mainly of outdoor origin, while the other VOCs, aldehydes and bacteria showed higher concentration indoors. Overheating, the lack of proper particle filters in the mechanical ventilation system, and low ACR and relative humidity were identified as frequent problems related to the building characteristics. The emissions from building materials and furniture, the proximity of construction works and unpaved roads, and the allergenic vegetation might considerably influence IAQ. The results highlight that risk reduction measures are needed to create healthier indoor environment in the Passive Houses.

Spatiotemporal Characteristics and Factors Influencing Urban Tourism Market Network in Western China: Taking Chengdu as an Example

Urban tourism network attention is important for measuring the competitiveness of the urban tourism industry, tourism attraction, and cultural soft power. In this study, we explored the spatiotemporal patterns and factors influencing network attention in the tourist source market and discussed how tourism cities can increase network attention, thus improving the competitiveness of urban cyberspace and developing soft power. Taking Chengdu as a research case, we obtained data on its tourism network attention from 31 provinces (autonomous regions and municipalities) between 2011 and 2021. We measured the spatiotemporal characteristics of network attention using the inter-annual change index, seasonal concentration index, potential tourists’ concentration coefficient, and ESDA model and analyzed the factors affecting spatiotemporal changes in network attention using the geographic weighted regression (GWR) model. The results revealed that from 2011 to 2021, the network attention of Chengdu tourism showed an overall “M”-type fluctuation trend, with significant seasonal differences and disequilibrium and significant differences in space, signifying an overall “∩”-shaped distribution trend. This suggested a weak negative spatial correlation. Further, the number of mobile Internet users, people in higher education per 100,000 people, per capita gross domestic product, urbanization rate, and passenger throughput are important factors that affect the network attention of Chengdu tourism. Thus, these results can be used by cities in western China to optimize the network attention rating system of urban tourism, strengthen the promotion of urban image, build a sustainable city, and transform network traffic into effective economic growth.

A note on the possibility of identifying Leydig and Sertoli cells by immunohistochemistry in bowhead whales (Balaena mysticetus)

Leydig cells have been found to be either unidentifiable or at apparent low numbers during routine histologic examination of bowhead whale testes. Therefore, formalin-fixed, paraffin-embedded testicular tissues from 14 bowhead whales were retrospectively examined to determine if immunohistochemical staining could aid in identification of Sertoli and Leydig cells. Multiple intratesticular samples were examined when available. Sertoli and Leydig cells were differentiated using inhibin and calretinin stains. Significant whale length and seasonal differences were not found; however, a trend toward increased staining intensity was noted for autumn harvested whales.

Multiscale feature analysis of forecast errors of 500 hPa geopotential height for the CMA‐GFS model

AbstractUsing ERA5 reanalysis data from March 2021 to February 2022 and the China Meteorological Administration Global Forecasting System (CMA‐GFS) operational forecast dataset of 500 hPa geopotential height in the Northern Hemisphere in the same period, the multiscale features of forecast errors are analyzed. The results indicate that the anomaly correlation coefficient (ACC) of 500 hPa geopotential height and its multiscale components in the Northern Hemisphere keep decreasing with the extension of forecast lead time, and there are no seasonal differences in the evolution of the ACC. The effective forecast skills by season for the CMA‐GFS model are above 6 days at multiscale, with the highest skills in winter and the planetary‐scale components. In space, significant seasonal differences are observed in the locations of the extreme values of multiscale forecast errors for 500 hPa geopotential height, and the spatial distribution of forecast errors reflects the inadequate prediction of the intensity of large‐scale trough and ridge systems at middle and high latitudes and the phase‐shift prediction of small troughs and ridges at middle latitudes. Generally, the forecast errors of the original field and planetary‐scale component show wavelike or banded distribution, and the synoptic‐scale forecast errors are always distributed in latitudinal wavelike patterns alternating between positive and negative, without significant differences in the distribution of land, sea, and terrain. The first empirical orthogonal function modes of multiscale forecast errors almost retain their respective feature. In temporal, the spring, summer, and autumn time series all have quasi‐biweekly positive and negative phase transitions within the monthly scale, and the significant phase transition in winter only occurs around January 1st. These results deepen the understanding of the distribution and possible causes of forecast errors of the CMA‐GFS model and provide ideas for the improvement and revision of the model.

Evaluation of the response of phytoplankton communities to heavy metal stresses using multi-statistical approaches, Alexandria coast, Egypt

This is the first study to investigate the antioxidant response of the phytoplankton community to heavy metal stress in the open sea. The levels of Cu, Zn, Cd, Mn, Pb, and Fe were determined in the phytoplankton community and water in Abu Talat (AT), and El Dekhella Harbour (DH), Alexandria coast during spring 2018 and autumn 2019. Furthermore, the changes in phytoplankton abundance, the bioconcentration factor (BCF), the metal pollution index (MPI), and the pollution load index (PLI) were used to assess pollution status. The phytoplankton density in the DH stations was 17–42 folds higher than in the AT. The order of BCF during spring was Fe > Zn > Pb > Cu > Mn > Cd. However, Fe > Zn > Mn > Cu > Pb > Cd was found in both areas during the autumn. The BCF, MPI, and PLI of the phytoplankton community revealed that DH is more polluted than AT. The phenol, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and total antioxidant activity of the phytoplankton communities achieved higher values in DH than in AT stations, in response to heavy metal stresses. Principal component analysis was performed to follow pollution sources, suggesting that station 3 in both seasons was a hot spot because of stagnant water. A correlation matrix indicated that phenol and DPPH interacted positively in both locations during the two seasons. Analysis of variances with replicate tests on heavy metals in phytoplankton revealed significant seasonal differences across the two areas at a significance level < 0.05.

Advancing Characterization and Modeling of Space-Time Correlation Structure and Marginal Distribution of Short-Duration Precipitation

The statistical characterization of precipitation (P) at short durations (≤ 24 h) is crucial for practical and scientific applications. Here, we advance the knowledge of and ability to model the space-time correlation structure (STCS) and marginal distribution of short-duration P using a network of rain gages in central Arizona with one of the largest densities and spatial coverages in the world. We separately analyze summer and winter P sampled at multiple durations, Δt, from 0.5 to 24 h. We first identify an analytical model and a three-parameter distribution that robustly capture the empirical STCS and marginal distribution of P, respectively, across Δt’s. We then conduct Monte Carlo experiments consisting of multisite stochastic simulations of P time series to explore the spatial and seasonal variability of these properties. Significant seasonal differences emerge, especially at low Δt. Summer (winter) P exhibits weak (strong) correlation structure and heavy- (light-)tailed distributions resulting from short-lived, isolated thunderstorms (widespread, long-lasting frontal systems). The STCS of P is most likely homogeneous and isotropic except for winter at Δt ≥ 3 h, where anisotropy could be introduced via the motion of frontal storms. The spatial variability of the marginal distribution is reproduced by a regional parameterization dependent on elevation in all cases except, again, for winter at Δt ≥ 3 h where additional factors are needed to explain the variability of the mean P intensity. This work provides insights to improve stochastic P models and validate convection-permitting models used to investigate the mechanisms driving changes in short-duration P.