Seasonal Characteristics Research Articles

Sea ice perturbations in aquaplanet simulations: isolating the physical climate responses from model interventions

Abstract Comprehensive climate model simulations with perturbed sea ice covers have been extensively used to assess the impact of future sea ice loss, suggesting substantial climate changes both in the high latitudes and beyond. However, previous work using an idealized energy balance model calls into question the methods that are used to perturb sea ice cover, demonstrating a consistent overestimate of the surface warming due to sea ice loss, while the large complexity gap between the idealized and comprehensive models makes the implications of this result unclear. To bridge this gap we have performed simulations with a new implementation of the CESM2 model in a slab-ocean aquaplanet configuration coupled with thermodynamic sea ice, which is able to capture the realistic seasonal characteristics of polar climate change. Using this model setup, we perform a suite of experiments to systematically quantify the spurious climate responses associated with melting sea ice without a CO2 forcing. We find that using the sea ice ghost flux method overestimates many aspects of the climate response by ~10-20%, including the polar warming, the mini global warming signal and the increase in both precipitation and evaporation. The location of the latitudinal band of heating applied to melt the sea ice relative to the midlatitude jet is important for determining where the midlatitude circulation response is overestimated. This work advances our ability to isolate the true climate response to sea ice loss, and provides a framework for conducting coupled sea ice loss simulations absent the spurious impacts from the addition of artificial heating.

Morphological and agronomic features of potato cv. Gardena highly resistant to Phytophthora infestans (Mont.) de Bary depending on the nitrogen dose

In a 2-year field study, the impact of mineral nitrogen fertilization on the productivity of a new potato cultivar, promising due to the highest resistance to potato late blight among the registered ones, was compared to the proven, widely cultivated Denar cultivar. The study determined morphological features (size and weight of organs), physiological indicators (cover of soil by leaves – LAI. leaf greenness – SPAD) of potato plants during the growing season, yield and quality characteristics of tubers and optimal level of nitrogen fertilization. Tuber quality was assessed based on the share of tuber size and external defects in the yield structure. Optimal mineral nitrogen fertilization was determined based on the relationship between the increase in tuber yield and the increasing dose of this ingredient. The research took into account two factors: nitrogen dose (0, 50 kg‧ha–1, 100 kg‧ha–1, 150 kg‧ha–1) and cultivar (Gardena and Denar). The increase in the dose of mineral nitrogen fertilization to 150 kg‧ha–1 resulted in a significant increase in plant height, the weight of the root system, stems, leaves and the share of large tubers in the yield. It was shown that the Gardena cultivar was characterized by greater requirements for mineral nitrogen fertilization, low effectiveness of its use, a higher share of large tubers (diameter above 60 mm) and lower tuber yield than the Denar cultivar. In a year characterized by excess rainfall, plants produced a greater mass of the root system and the mass of the above-ground part, and in a year with an amount of rainfall close to optimal the final yield of tubers and the share of large tubers in the yield were higher.

Two decades of aerosol trends over India: seasonal characteristics and urban-rural dynamics

Abstract India faces significant air quality challenges, with one of the highest air pollution levels of any country in the world. Here, we examine two decades (2001–2019) of both particulate matter (PM2.5) concentration and aerosol optical depth (AOD) over the country. Increases are seen between the two decadal averages, for 2001–2010 and 2011–2019, in western India, particularly in the Indo-Gangetic Plain (IGP). IGP region, including Bihar, West Bengal, Jharkhand, and Uttar Pradesh, shows the highest increases in AOD (+0.03, 13%) and PM2.5, s (+8 µg m−3). Seasonal AOD patterns fluctuate, with the IGP experiencing the highest wintertime increase, especially in Bihar (+0.07). In summer, there are increases in AOD along the southern and eastern coastal areas. Monsoons cause a slight rise in AOD, except in Rajasthan. In the post-monsoon season, the IGP experiences a notable increase in AOD (+0.057, 25%), potentially driven by biomass burning in Bihar (+0.11) and Uttar Pradesh (+0.075). Dividing our study area into urban and peri-urban clusters (n = 2791), AOD is found to be similar, possibly due to advective mixing. However, the differences between urban and rural areas become more noticeable, especially in the second decade. Correlations between AOD and PM2.5, g vary across locations, with the highest found in Kanpur (R 2 = 0.61) and weaker in Delhi (R 2 = 0.42), highlighting the need for more ground monitoring. However, it suggests that satellite-derived AOD can generally be used to examine trends in PM2.5 over longer time frames.

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Locally Defined Seasonal Rainfall Characteristics within the Horn of Africa Drylands from Rain Gauge Observations

Abstract Seasonal rainfall is critical to lives and livelihoods within the Horn of Africa drylands (HAD), but it is highly variable in space and time. The main HAD rainfall seasons are typically defined as March–May (MAM) and October–December (OND). However, these 3-month periods are only generalized definitions of seasonality across the HAD, and local experience of rainfall may depart from these substantially. Here, we use daily rain gauge data with a duration of at least 10 years from 69 stations across the drylands of Kenya, Somalia, and Ethiopia to locally delineate key rainfall seasons. By calculating local seasonal rainfall timings, totals, and extremes, we obtain more accurate estimates of the spatial variability in rainfall delivery across the HAD, as well as climatological patterns. Results show high spatial variability in season onset, cessation, and length across the region, indicating that a homogenous classification of rainfall seasons across the HAD (e.g., MAM and OND) is inadequate for representing local rainfall characteristics. Our results show that the “long rains” season is not significantly longer than the “short rains” season over the period of study. This could be related to the previously documented decline of the “long rains” seasonal totals over recent decades. Several rainfall metrics also vary spatially between seasons, and the rainfall on the most extreme days can accumulate to double the local mean seasonal total. The locally defined rainfall seasons better capture the bulk of the rainfall during the season, giving improved characterization of rainfall metrics, consistent with the aim of a better understanding of rainfall impacts on local communities.

Seasonal Herbage-Livestock Balance and Grassland Pressure Index Analysis in the Yellow River Source Park, Tibet Plateau, China.

Grassland carrying capacity is an indicator for measuring the stability of grassland ecosystems and can provide a basis for formulating regional sustainable grazing strategies. However, most previous studies on this have only considered annual fluctuations, but seasonal changes were ignored. In this study, the herbage yield and nutrient value of two grassland types in Yellow River Source Park (YRSP) were measured by sampling point survey method in four seasons, and the seasonal and annual grassland carrying capacity, carrying numbers of standard sheep unit (SU) were estimated based on the dry matter (DM) content, crude protein, and metabolic energy of herbage. Due to the carrying capacity being low during the yellow and wintering periods, we combined them and calculated the carrying capacity for only three periods, including the flourishing period, greening period, and withering period. The grassland pressure index (GPI) was measured by the ratio of the actual standard sheep number and the calculated number. The results showed that the herbage yield and nutrient output were higher in spring and summer, lower in autumn and winter, and showed a tendency for alpine meadows to be higher than alpine grasslands during the flourishing period (p < 0.05). The unit area carrying capacity varied significantly with the season and showed the seasonal changing characteristics of the flourishing period > greening period > withering period. The seasonal carrying number was much higher than the actual carrying number during the flourishing period and much lower than the actual carrying number during the withering period. In terms of annual carrying capacity, the GPI was balanced when considering livestock alone, while critical overloaded when considering livestock and wildlife. This study suggested the influence of seasonal change on the grassland carrying capacity should be fully considered in the grassland utilization. Meanwhile, the feeding needs of livestock and wildlife should be taken into account, and timely supplemented when forage is in short supply.

Accumulation of sodium and manganese during litter decomposition of Qinghai spruce (Picea crassifolia) forest in China

AbstractLitter decomposition is a key ecological process as a control on nutrient cycling in forest ecosystems. Forest litter is an important carrier of element cycles in forest ecosystems. The biogeochemical cycle of elements is of great significance to plant communities and ecosystem functions. At present, most of the studies on the litter major elements have focused only on C, N, and P, and there are few studies on the biogeochemical cycle of sodium (Na) and manganese (Mn) of forest litter. In this study, we investigated the concentrations and fluxes of Na and Mn in needle litter of Qinghai spruce over a 12‐month period. We measured the release and accumulation dynamics of litter Na and Mn during litter decomposition at different canopy coverage and elevations over 3.9 years. The results show that the annual Na and Mn concentrations were 312.34 and 621.19 mg kg−1, and the annual fluxes of Na and Mn were 236.67 and 343.16 g ha−1 in needle litter, respectively. After 3.9 years of litter decomposition, Na released 15.6% and Mn accumulated 93.4% compared to the initial concentration. The concentration of Na and Mn in needle litter both accumulated fastest at low canopy coverage. The concentrations of Na and Mn were different at various elevations, but there was no obvious regularity. These results illustrate the seasonal characteristics of litter element return and nutrient cycling during litter decomposition in typical coniferous forests in the Qilian Mountains and could provide basic data and theoretical basis for ecological protection in the Qilian Mountains.

Spatial and seasonal abundance and characteristics of microplastics along the Red River to the Gulf of Tonkin, Vietnam

This study aimed to examine the occurrence of microplastics in surface water and sediment samples collected from Hanoi to the Ba Lat estuary along the Red River, the second-largest river in Vietnam (surface area: 156,451 km2). 21 stations were sampled during the dry (March 2023) and rainy (September 2023) seasons. The analytical procedure involved: digestion with hydrogen peroxide, flotation with potassium carbonate, and overflow filtration. The filters were analyzed by microscopy (Nikon SMZ645) to describe shapes and colors and by μ-FTIR (PerkinElmer Spotlight 400) to determine polymer types and abundances. Results showed that microplastic quantities throughout the river ranged from 10 to 203 items.m−3 in surface water and from 653 to 8069 and 990 to 21,610 items.kg−1 dried weight (d.w.) in sediment during the rainy and dry seasons, respectively. MPs were classified into two main shape groups: fiber and fragment, with fibers being predominant, representing 82.0 % and 75.5 % of microplastics in water and sediment, respectively. The primary colors identified were white/transparent, black, and blue. Particles between 13 and 200 μm were the predominant size class, accounting for 64.1 % and 72.4 % of the microplastics in water and sediment, respectively. Polyethylene, polypropylene, and polyethylene terephthalate were the main polymers, accounting for 71.5 % and 72.2 % of the microplastics in water and sediment, as revealed by μ-FTIR analyses. Overall, in the Red River, the MP pollution load is moderate, but the type of particles detected represents a high to dangerous polymer risk, resulting in a very high potential ecological risk on the river.

Construction and Application Research of Beer Category Sales Forecasting Model Based on Big Data Analysis for Supermarket X

This study constructs a sales forecasting framework incorporating multi-source data based on beer category sales data from Supermarket X. The research collected beer sales data from 2020-2023, integrating temperature data, holiday information, and promotional activity data through an improved LSTM-based deep learning model. The study innovatively introduced Attention Mechanisms into the prediction model and proposed a dynamic weight allocation method for seasonal features, effectively enhancing the integration of heterogeneous data. Experimental results demonstrate excellent prediction performance across different time scales, achieving accuracy rates of 95%, 97%, and 98% for daily, weekly, and monthly forecasts respectively. Through practical validation in five demonstration stores, the model application improved inventory turnover by 25%, reduced stockout rates by 35%, and lowered operating costs by 15%. The research findings provide effective decision support tools for retail enterprises' refined operations management while suggesting future research directions regarding limitations in seasonal product forecasting and extreme weather response, offering practical reference for regional retail industry's intelligent transformation.

Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics.

Wastewater-based surveillance (WBS) is an emerging tool for monitoring the spread of infectious diseases, such as SARS-CoV-2, in community settings. Environmental factors, including water quality parameters and seasonal variations, may influence the prevalence of viral particles in wastewater. This study aims to explore the relationships between these factors and the incidence of SARS-CoV-2 across 28 monitoring sites, spanning different seasons and water strata. Samples were collected from 28 sites, accounting for seasonal and spatial (surface and intermediate water layers) variations. Key physicochemical parameters, heavy metals, and minerals were measured, and viral presence was detected using RT-qPCR. After data preprocessing, correlation analyses identified 19 relevant environmental parameters. Unsupervised learning algorithms, including K-means and K-medoid clustering, were employed to categorize the data into four distinct clusters, revealing patterns of viral positivity and environmental conditions. Cluster analysis indicated that seasonal variations and water quality characteristics significantly influenced SARS-CoV-2 positivity rates. The four clusters demonstrated distinct associations between environmental factors and viral prevalence, with certain clusters correlating with higher viral loads in specific seasons. The clustering patterns varied across sample sites, reflecting the diverse environmental conditions and their influence on viral detection. The findings underscore the critical role of environmental factors, such as water quality and seasonality, in shaping the dynamics of SARS-CoV-2 prevalence in wastewater. These insights provide a deeper understanding of the complex interplay between environmental contexts and disease spread. By utilizing WBS and advanced data analysis techniques, this study offers a robust framework for future research aimed at enhancing public health surveillance and interventions.

Two-stage stochastic robust optimization for capacity allocation and operation of integrated energy systems

To address the challenges posed by various uncertainties in integrated energy systems (IES) for planning and operation, this paper considers the capacity configuration of IES equipment with energy storage systems under a stepped carbon trading mechanism, as well as the planning of electric vehicle (EV) charging stations under different charging modes. A two-stage stochastic robust planning method is proposed, taking into account both short- and long-term uncertainties in renewable energy generation and electric, thermal, and cooling loads. In the first stage, planning decisions are made with the objective of minimizing investment costs, while accounting for the seasonal characteristics of the “source-load” relationship in the planning phase. Stochastic programming is employed to handle long-term uncertainties. In the second stage, operational simulation is performed with the goal of minimizing energy dispatch costs and carbon trading costs, and short-term uncertainties in “source-load” operations are described using robust optimization. The nested column-and-constraint generation (NC&CG) algorithm is used to solve this two-stage model. Finally, the proposed model is applied to an IES in northern China. The results show that orderly EV charging within the IES can reduce both investment and operational costs, as well as carbon emissions. The consideration of a stepped carbon trading mechanism can reduce system carbon emissions and enhance environmental sustainability. IES planning with multiple energy storage types is more economical than with a single energy storage type, and the proposed stochastic robust planning method, which considers both long- and short-term uncertainties, demonstrates stronger reliability and economic performance under extreme conditions.

Noise Variation Characteristics of the Superconducting Gravimeter at Jiufeng Station in Wuhan (China).

The noise level of gravity stations is an important indicator for measuring the operating status of a station and is a prerequisite for evaluating whether the station's observations can be used to extract weak geodynamic signals. With the continuous expansion of areas of human activity, gravity stations originally located in the wild may become increasingly closer to cities. Whether their noise levels change is an important issue that is worthy of attention. Based on power spectrum analyses and probability density function methods, the noise level of the superconducting gravimeter (SG) at Jiufeng station in Wuhan in the seismic frequency band of 0.001-0.04 Hz was calculated, and its time-varying characteristics were analyzed. The noise level of Jiufeng station did not change significantly before and after the lockdown of Wuhan due to the COVID-19 epidemic in 2020. No significant changes in the noise level were found before and after the official operation of Wuhan Metro Line 19 at the end of 2023. From October 2016 to April 2017, the noise level showed an abnormal trend of suddenly rapidly rising and then slowly declining, which was found to be caused by a tilt problem in the gravity sensor. Overall, in the seismic frequency band of 0.001-0.04 Hz, the noise level at Jiufeng station showed seasonal variation characteristics, and the noise was stronger in winter than in summer, which is consistent with the characteristics of Earth's hum. Since January 2022, the noise level has shown an increasing trend year by year. The results of this study can provide an important reference for the operation of gravity stations and the extraction of weak geodynamic signals.

Development of seasonal ozone maximum reactivity scales for Beijing, China

We designed scenarios based on urban emissions to develop a Maximum Incremental Reactivity (MIR) scale for 41 conventional volatile organic compounds (VOCs) in Beijing and quantitatively analyzed the various factors affecting MIR in urban areas. We explored the effects of updating the representative urban atmosphere used for ozone reactivity calculations by incorporating updated meteorological data, emission rates, initial condition concentrations, and VOC profile compositions. A box model equipped with the Master Chemical Mechanism (MCM) was used to localize the MIR scale for Beijing based on urban emission model inputs, and this was compared with scenarios in the United States. The MIR differences for most VOC species were minimal; however, Beijing exhibited higher MIR values for aromatic hydrocarbons in the summer, attributed to the higher emission intensity of aromatics in the city. Due to differences in meteorological conditions and emission scenarios, the MIR showed significant seasonal characteristics across different months. It is necessary to calculate independent MIR scales for different urban agglomerations under specific ozone pollution seasons and emission conditions. This study provides recommendations for the application of the MIR scale and can serve as a reference for VOC control strategies in China and other countries facing ozone pollution.

Long-Term Changes in Light Attenuation in the Sea of Marmara

Primary production, which drives the development of marine ecosystems, is largely dependent on the light present in the water column. Therefore, understanding the attenuation coefficient (Kd (PAR)) is crucial for describing underwater light variability. This study reveals the regional and seasonal characteristics of light attenuation in the Sea of Marmara for the first time. A 10-year time series (2000-2009) of Kd from the northeastern Sea of Marmara was analyzed and compared with seasonal values from the entire Sea of Marmara in 2021. Between 2000 and 2009, the coefficient ranged from 0.139 to 0.539 m⁻¹, with a mean of 0.220 m⁻¹ and a standard deviation of 0.063. The highest values occurred in winter and spring, while the lowest were in autumn and summer. Variations in attenuation coefficients were inversely related to the euphotic zone depth. In 2021, Kd values ranged from 0.123 to 0.443 m⁻¹, with a mean of 0.247 m⁻¹. During the summer of 2021, when mucilage was present, the attenuation coefficient was lowest at the surface and increased with depth, indicating that larger mucilage aggregates affect light penetration significantly. Coastal areas and bays were classified as turbid, while deeper areas were moderately turbid based on the attenuation coefficient. This work will serve as an important reference for research on marine ecosystems, water quality, modeling, remote sensing and climate change, and will improve our understanding of their impacts at the ecosystem level. These insights are vital for both science and policy, particularly in the areas of conservation, resource management and climate resilience. The results can lead to practical outcomes such as improved monitoring tools, better predictive models and more effective management strategies.

Heterogeneous Seasonal Deformation and Strain Budget in Himachal, NW Himalaya from new cGPS measurements: Hydrological and Seismic Hazard Implications

GPS measurements from 10 new permanent sites installed in Himachal, NW Himalaya are analysed with the primary objective to decipher the seasonal crustal deformation characteristics, its origins and hydrological implications. Additionally, we focus on the seismic hazard implications of the seasonal transients as well as of the long-term secular motion of GPS sites. Our findings suggest that the global hydrological loading models can relatively well explain the temporal surface displacements resulting from the monsoon rainfall in the Indo-Gangetic plains (IGP) but poorly account for the winter snowfall in Higher Himalaya. The approximately elliptical horizontal seasonal motion of GPS sites seems to be controlled by the spatio-temporal variations in the monsoonal hydrological load over the IGP. The hydrological process derived from the GPS data suggests slow and steady recovery in the water storage in Himachal Himalaya. The more intriguing phenomena is reflected in the unusually high seasonal site uplift and the yearlong (mid-2020 – mid-2021) plunge in the water storage corresponding to the time-period of COVID-19 lockdown phases, which could be speculated as its indirect impact on the crustal deformation history. The seasonal strain analysis revealed significant spatial heterogeneity. The arc-normal and arc-parallel seasonal strain resulting from differential seasonal motion between GPS sites seems to be primarily localized in the vicinity of locking transition zone, while the locked segment is being largely translated without experiencing significant strain. The localized seasonal strain showed positive correlation with the background seismicity rate suggesting possible modulation of the earthquake nucleation process. The regional seismic hazard assessment suggests susceptibility to a future great earthquake of ∼Mw8.4. The additional observed equivalency between the updated strain budget (∼9.3 m) and the previously reported co-seismic displacement (∼9.3 m) during the last great earthquake around ∼1400–1500 CE, suggests a recurrence interval of ∼600 years for great earthquake events in Himachal Himalaya with significant reliability.

Seasonal and Diurnal Characteristics and Drivers of Urban Heat Island Based on Optimal Parameters-Based Geo-Detector Model in Xinjiang, China

In the context of sustainable urban development, elucidating urban heat island (UHI) dynamics in arid regions is crucial. By thoroughly examining the characteristics of UHI variations and potential driving factors, cities can implement effective strategies to reduce their impacts on the environment and public health. However, the driving factors of a UHI in arid regions remain unclear. This study analyzed seasonal and diurnal variations in a surface UHI (SUHI) and the potential driving factors using Pearson’s correlation analysis and an Optimal Parameters-Based Geographic Detector (OPGD) model in 22 cities in Xinjiang, northwest China. The findings reveal that the average annual surface urban heat island intensity (SUHII) values in Xinjiang’s cities were 1.37 ± 0.86 °C, with the SUHII being most pronounced in summer (2.44 °C), followed by winter (2.15 °C), spring (0.47 °C), and autumn (0.40 °C). Moreover, the annual mean SUHII was stronger at nighttime (1.90 °C) compared to during the daytime (0.84 °C), with variations observed across seasons. The seasonal disparity of SUHII in Xinjiang was more significant during the daytime (3.91 °C) compared to nighttime (0.39 °C), with daytime and nighttime SUHIIs decreasing from summer to winter. The study also highlights that the city size, elevation, vegetation cover, urban form, and socio-economic factors (GDP and population density) emerged as key drivers, with the GDP exerting the strongest influence on SUHIIs in cities across Xinjiang. To mitigate the UHI effects, measures like urban environment enhancement by improving surface conditions, blue–green space development, landscape optimization, and economic strategy adjustments are recommended.

Study and optimization of the influence of the water tank temperature on the performance of a solar assisted multi-source heat pump drying system

Solar assisted air source heat pump drying (SASHPD) system has been widely studied due to its excellent energy saving and high dried quality of product. To improve system performance, the control logic of a solar assisted multi-source heat pump drying (SMSHPD) system was investigated, and the simulation model and the experimental platform were established in this paper. The seasonal operating characteristics of the air source heat pump drying (ASHPD), the SASHPD and the SMSHPD modes were studied. The energy consumption, coefficient of performance (COP) and specific moisture extraction rate (SMER) of the three drying systems were calculated and analyzed. Additionally, the water tank temperature were also measured to determine the seasonal control logic of the drying system. It was found that compared to the ASHPD system, in summer, the energy consumption of the SASHPD system was reduced by 33.06 %, and COP and SMER was increased by 49.4 % and 49.38 %, respectively. Moreover, because of the high ambient temperature and water tank temperature, the experimental results indicated that only the SASHPD control logic should be implemented in summer. The maximum error of the simulation results was 9.1 % and the accuracy of the simulation model was confirmed. In autumn, the lower water tank temperature after drying illustrated that the solar energy can be more fully utilized, which explained why the performance of the SMSHPD mode was increased by 6.5 % compared to the SASHPD mode. Consequently, it is necessary to implement the SMSHPD control logic in autumn. Moreover, the operating temperature of the water source heat pump drying mode in autumn was optimized and the optimal temperature range was 28–54 °C. The average COP in one week after optimization was 6.21 % higher than that of the original operating temperature. Based on the validated simulation model, the optimal operating temperatures in spring and winter were also calculated and the control logic of the SMSHPD system operating in the four seasons were all obtained. This paper has important guiding significance for the reasonable choice of the control logic of a solar combined air source heat pump drying system.

Pollution characteristics and health risk assessment of heavy metals in PM2.5 in Fuxin, China

Fuxin is located in the atmospheric channel around Bohai Bay, and its geographical location is very special. Few existing studies have investigated the pollution characteristics and health risk assessment of heavy metals in atmospheric PM2.5 during the four seasons in Fuxin, so a total of 180 PM2.5 samples were collected from four sampling sites in Fuxin from December 2021 to November 2022. The seasonal distribution characteristics of V, Cr, Mn, Co, Ni, Cu, Zn, Pb, As, Sb, Cd and Ba were analysed via inductively coupled plasma-mass spectrometry (ICP‒MS), and the source of the heavy metals was analysed via the enrichment factor (EF) and principal component analysis (PCA). A health risk model was used to assess the health risk of respiratory exposure in men, women and children in Fuxin. The results revealed that the annual average mass order of heavy metals in Fuxin PM2.5 was Zn (0.2947 μg·m−3) > Pb (0.0664 μg·m−3) > As (0.0225 μg·m−3) > Ba (0.0205 μg·m−3) > Mn (0.0187 μg·m−3) > Cu (0.0140 μg·m−3) > Cr (0.0095 μg·m−3) > V (0.0067 μg·m−3) > Ni (0.0061 μg·m−3) > Sb (0.0024 μg·m−3) > Cd (0.0019 μg·m−3) > Co (0.0007 μg·m−3). The annual average concentration of As was 3.75 times the GB3095-2012 (China) secondary standard limit, and the concentration of hazard quotient (HQ) in PM2.5 was lower than 1, but the concentration of incremental lifetime cancer risk (ILCR) in As was higher than the cancer risk threshold (10–4). These findings indicate a certain risk of cancer in the urban population of Fuxin. Therefore, it is necessary to control the emissions created from coal burning to minimize the health risks to the people of Fuxin.

Epidemiological and clinical characteristics of respiratory syncytial virus infections in children in Jiangsu Province, 2014-2023

Objective: To analyze the epidemiological and clinical characteristics of respiratory syncytial virus (RSV) infection in children in Jiangsu Province from 2014 to 2023. Methods: The acute respiratory infection cases in children aged 0-14 years were selected from outpatient/emergency or inpatient departments in 2 surveillance sentinel hospitals, respectively, in Nanjing, Suzhou and Taizhou of Jiangsu from 1 July 2014 to 31 December 2023, and RSV nucleic acid test was conducted and the intensity of the RSV infection was accessed by WHO influenza epidemiological threshold method, and case information and clinical data were collected. χ2 test was used to compare the differences between groups, and the Bonferroni method was used for pairwise comparisons between groups. Results: In 4 946 cases of acute respiratory infections, the RSV positive rate was 8.21% (406/4 946), and the age M(Q1,Q3) of the cases was 1 (0, 3) years. The RSV positive rate was 10.92% (258/2 362) during 2014-2019 and 6.06% (118/1 948) during 2019-2023, the difference was significant (χ2=31.74, P<0.001). RSV infection mainly occurred from October to March during 2014-2019, with the incidence peak in December and moderate or higher intensity. The seasonality of RSV infection was not obvious during 2019-2023, with low intensity. The RSV positive rate was highest in children in age group 0- years (17.85%, 151/846), and the positive rate declined gradually with age (χ2=184.51, P<0.001). The RSV positive rate was higher in inpatient cases (9.84%, 244/2 480) than in outpatient/emergency cases (6.57%, 162/2 466) (χ2=17.54, P<0.001). In the 155 RSV infection cases with complete clinical data, the clinical symptoms mainly included cough (99.35%, 154/155), fever (55.48%, 86/155), and shortness of breath (45.16%, 70/155). In the cases aged <6 months, the proportion of those with fever was low, but the proportion of those with shortness of breath, transferred to intensive care units, and receiving oxygen therapy were higher (all P<0.05). Children aged <6 months and those with underlying diseases were more likely to have severe RSV infection (all P<0.05). Conclusions: RSV infection in children in Jiangsu Province showed seasonal prevalence in winter from 2014 to 2019. Since 2020, the seasonal characteristics of the epidemic have changed, the epidemic period has been dispersed and the epidemic intensity has decreased. Infants <1 year old were at high risk for RSV infection, and those <6 months old and with underlying diseases might have severe infection.

Quantitative comparison of seasonal and diurnal characteristics for urban heat using meteorological monitoring data

Quantitative comparison of seasonal and diurnal characteristics for urban heat using meteorological monitoring data