Spatio-temporal distribution of micropropagules of green algae along the Jiangsu coast.

We conducted continuous monitoring at 13 stations along the Jiangsu coast to study the spatiotemporal distribution, population succession of micropropagules of green algae, and their impact on the outbreak of Southern Yellow Sea green tide. The study discovered that: 1) Green algae micropropagules had obvious temporal and spatial distribution and population changes along the Jiangsu coast. The monthly average abundance of micropropagules of green algae at station BH1, which was the high-value area, was 1230 inds/L. Station XS2 had the second-highest value area. Green algae micropropagules had an average monthly abundance of 836 inds/L. Between stations XS2 and BH1, the amount of green algae micropropagules steadily declined in comparison to other stations. The abundance was greatest from spring to early summer, and Ulva prolifera micropropagules predominated; 2) Compared with salinity, temperature had a more obvious effect on the micropropagules of green algae along the Jiangsu coast; 3) Green algae micropropagules on the Jiangsu coast could be a potential additional source on the outbreak of Southern Yellow Sea green tide. More data are needed to corroborate this conclusion. For the purpose of preventing and managing green tide, it is crucial to investigate the Southern Yellow Sea's potential supplementary source. This study analyzes the spatiotemporal distribution and population changes of green algae micropropagules along the Jiangsu coast, as well as their impact on green tide outbreaks, providing scientific data support for the prevention and control of green tides in the Southern Yellow Sea.

Dynamics changes of coastal aquaculture ponds based on the Google Earth Engine in Jiangsu Province, China.

Monitoring the spatiotemporal variation in coastal aquaculture zones is essential to providing a scientific basis for formulating scientifically reasonable land management policies. This study uses the Google Earth Engine (GEE) remote sensing cloud platform to extract aquaculture information based on Landsat series and Sentinel-2 images for the six years of 1984 to 2021 (1984, 1990, 2000, 2010, 2016 and 2021), so as to analyze the changes in the coastal aquaculture pond area, along with its spatiotemporal characteristics, of Jiangsu Province. The overall area of coastal aquaculture ponds in Jiangsu shows an increasing trend in the early period and a decreasing trend in the later period. Over the past 37 years, the area of coastal aquaculture ponds has increased by a total of 54,639.73 ha. This study can provide basic data for the sustainable development of coastal aquaculture in Jiangsu, and a reference for related studies in other regions.

Highway Deformation Monitoring by Multiple InSAR Technology.

Addressing the challenge of large-scale uneven deformation and the complexities of monitoring road conditions, this study focuses on a segment of the G15 Coastal Highway in Jiangsu Province. It employs PS-InSAR, SBAS-InSAR, and DS-InSAR techniques to comprehensively observe deformation. Analysis of 73 image datasets spanning 2018 to 2021 enables separate derivation of deformation data using distinct InSAR methodologies. Results are then interpreted alongside geological and geomorphological features. Findings indicate widespread deformation along the G15 Coastal Highway, notably significant settlement near Guanyun North Hub and uplift near Guhe Bridge. Maximum deformation rates exceeding 10 mm/year are observed in adjacent areas by all three techniques. To assess data consistency across techniques, identical observation points are identified, and correlation and difference analyses are conducted using statistical software. Results reveal a high correlation between the monitoring outcomes of the three techniques, with an average observation difference of less than 2 mm/year. This underscores the feasibility of employing a combination of these InSAR techniques for road deformation monitoring, offering a reliable approach for establishing real-time monitoring systems and serving as a foundation for ongoing road health assessments.

The deviation of farmers' willingness and behavior of domestic waste separation: a study on neighborhood effects and policy interventions.

Based on the perspective of combining informal and formal systems, this paper empirically explores the impact of neighborhood effects and policy interventions on the deviation of farmers' willingness and behavior of domestic waste separation (DWS) by using data from the China Land Economy Survey (CLES) and constructing a probit model. It should be explained that the neighborhood effect in this paper refers to the fact that the behavior of farmers is highly susceptible to the behavior of their neighbors in the process of production and living. The results of the study show that neighborhood effects and policy interventions have a significant negative impact on the deviation of farmers' willingness and behavior of DWS, respectively. Comparison of marginal effects shows that neighborhood effects > environmental advocacy > reward and punishment policies. From the interaction effects as a whole, neighborhood effects and policy interventions have complementary effects on the deviation of farmers' willingness and behavior of DWS, with the complementary effects of neighborhood effects and environmental advocacy being more significant. Heterogeneity analysis reveals that neighborhood effects completely replace the inhibitory effect of policy interventions on the deviation of high-income farmers' willingness and behavior of DWS, but have no effect on political elite farmers. The interaction between neighborhood effects and policy interventions has complementary effects on low-income farmers and ordinary farmers, with the complementary effects of neighborhood effects and environmental advocacy being more significant.

The changing health effects of air pollution exposure for respiratory diseases: a multicity study during 2017-2022.

BACKGROUND: Multifaceted SARS-CoV-2 interventions have modified exposure to air pollution and dynamics of respiratory diseases. Identifying the most vulnerable individuals requires effort to build a complete picture of the dynamic health effects of air pollution exposure, accounting for disparities across population subgroups. METHODS: We use generalized additive model to assess the likely changes in the hospitalisation and mortality rate as a result of exposure to PM2.5 and O3 over the course of COVID-19 pandemic. We further disaggregate the population into detailed age categories and illustrate a shifting age profile of high-risk population groups. Additionally, we apply multivariable logistic regression to integrate demographic, socioeconomic and climatic characteristics with the pollution-related excess risk. RESULTS: Overall, a total of 1,051,893 hospital admissions and 34,954 mortality for respiratory disease are recorded. The findings demonstrate a transition in the association between air pollutants and hospitalisation rates over time. For every 10 µg/m3 increase of PM2.5, the rate of hospital admission increased by 0.2% (95% CI: 0.1-0.7%) and 1.4% (1.0-1.7%) in the pre-pandemic and dynamic zero-COVID stage, respectively. Conversely, O3-related hospitalization rate would be increased by 0.7% (0.5-0.9%) in the pre-pandemic stage but lowered to 1.7% (1.5-1.9%) in the dynamic zero-COVID stage. Further assessment indicates a shift of high-risk people from children and young adolescents to the old, primarily the elevated hospitalization rates among the old people in Lianyungang (RR: 1.53, 95%CI: 1.46, 1.60) and Nantong (RR: 1.65, 95%CI: 1.57, 1.72) relative to those for children and young adolescents. Over the course of our study period, people with underlying diseases would have 26.5% (22.8-30.3%) and 12.7% (10.8-14.6%) higher odds of having longer hospitalisation and over 6 times higher odds of deaths after hospitalisation. CONCLUSIONS: Our estimates provide the first comprehensive evidence on the dynamic pollution-health associations throughout the pandemic. The results suggest that age and underlying diseases collectively determines the disparities of pollution-related health effect across population subgroups, underscoring the urgency to identifying the most vulnerable individuals to air pollution.

Inequality characteristics and influencing factors of CO2 emissions per capita in Jiangsu Province, China.

Analyzing the inequality characteristics and influencing factors of CO2 emissions per capita (CEPC) is conducive to balancing regional development and CO2 emissions reduction. This study applied the Gini coefficient and Theil index to investigate the CEPC inequalities during 2005-2017 at the county level in Jiangsu Province, China. Considering the spatial spillover and interaction effects, the factors influencing CEPC were analyzed by a hierarchical spatial autoregressive model. The results showed that the inequalities in CEPC first increased and then decreased at the inter-regional, and inter-county levels. The spatial pattern of CEPC was stable, and there was a significantly positive spatial autocorrelation of CEPC at the county level. The High-High type counties were mainly located in Sunan (southern Jiangsu). The spatial interaction effects of the CEPC between the prefecture and county levels indicated that governments at the prefecture level should integrate their county governments to reduce the CEPC. Moreover, carbon intensity, GDP per capita, land urbanization, and industrial structure play an important role in reducing CEPC. Our findings provide a scientific basis for formulating reasonable and effective carbon emission reduction policies.

Surveillance of noise exposure level in industrial enterprises-Jiangsu Province, China, 2022.

Occupational noise exposure is the most prominent problem in industrial enterprises in Jiangsu Province. Since 2019, China has established the National Surveillance System for Occupational Hazards in the workplace to grasp the current occupational hazards in critical industries, including occupational noise. According to the Work Plan for Surveillance of Occupational Hazards in the Workplace (2022) issued by the National Health Commission of the People's Republic of China, the noise exposure level of 3,142 enterprises in our province was monitored, the median and interquartile range (IQR) were calculated, and the distribution of noise exposure level was described by industry classification, enterprise-scale and ownership type of the enterprise. The railway, shipping, aerospace, and other transportation equipment manufacturing industries exhibited the highest proportion (42.9%) of individual noise exposure levels exceeding 85 dB(A), followed by the motor vehicles manufacturing industry (36.4%). The proportion of individual noise exposure levels exceeding 85 dB(A) was higher in medium and small enterprises, with rates of 28.1 and 28.6%, respectively. The highest proportion of personal noise exposure levels exceeding 85 dB(A) was observed in Hong Kong, Macao and Taiwan investment enterprises (37.5%), followed by incorporated companies (34.6%) and limited liability companies (28.1%), the lowest was state-owned enterprises(15.5%). The areas with excessive noise are primarily concentrated in grinding, welding, machining, cutting, and other related operations, accounting for 61.2% of the total. Among these operations, grinding accounts for 29.8%. The highest environmental noise and individual noise intensity were found in sandblasting and grinding positions, with individual noise intensities of 115.5 dB(A) and 108.4 dB(A), respectively. The noise exposure risk is so high that cannot be ignored in the manufacturing industry, especially in Hong Kong, Macao, and Taiwan investment enterprises, incorporated companies and medium and small enterprises.

Investigation and characterization of rice dwarfing epidemic caused by southern rice black-streaked dwarf virus in Jiangsu in 2023.

During the field surveys in Jiangsu Province, China, contiguous patches of rice plants with varying degrees of dwarfing, wax-white or dark brown enations at the base of stems, and abnormal heading symptoms were observed in the fields located in Jiangning District in Nanjing City, Jurong County in Zhenjiang City, and Zhangjiagang County in Suzhou City. Through molecular analyses, the presence of southern rice black-streaked dwarf virus was confirmed in symptomatic rice plants. The infections of other rice viruses that cause dwarfing were also ruled out. Additionally, Koch's postulates were fulfilled, further validating SRBSDV as the causal agent for the observed dwarfing disease epidemic. Furthermore, the phylogenetic analyses revealed that the SRBSDV prevalent in Jiangsu in 2023 may originate from multiple regions in Vietnam. Our study has documented the emergence of an SRBSDV epidemic in Jiangsu in 2023, marking the first incidence of southern rice black-streaked dwarf disease in this region.

Temporal variation and pollution status of heavy metals in core sediments from Jiangsu intertidal area, China.

Eight heavy metal concentrations were analyzed from a 60.35-m-long sediment core in the Jiangsu intertidal area, China. Based on the lithofacies characteristics, mean grain size, downcore distributions of elements, and accelerator mass spectrometry (AMS) 14C and optically stimulated luminescence (OSL) data, the sediments in the core were divided into three units that formed during marine isotope stages 4 (MIS 4), MIS 3, and MIS 1. Except for Cd, all the other heavy metals had the lowest average concentrations in U3, which formed during MIS 4 with the coarsest sediment, representing a fluvial deposit. Most of the heavy metals were positively correlated with Al, Fe, and the total organic carbon (TOC), indicating these metals had the same sources. Pearson's correlation coefficient, enrichment factor, geoaccumulation index, and principal component analysis suggested that there was no element enrichment or contamination in the core sediments and that all heavy metals were naturally sourced.

Occurrence, distribution, and risk assessment of pesticides in surface water and sediment in Jiangsu Province, China.

The occurrence and distribution of 157 pesticides were investigated in surface water and sediment in Jiangsu Province, China. Gas chromatography-mass spectrometry was used to analyze and quantify these pesticides, and the risk quotient method was used to evaluate their respective environmental risk. The results showed that 91 pesticides were detected in surface water. The organophosphates (OPPs), fungicides, and amide herbicides were predominant. The total concentration in surface water ranged from 63.7 to 22,463 ng/L, 3.90 to 7262 ng/L, and ND to 34,120 ng/L, respectively. The mean concentration was 3479 ng/L, 1644 ng/L, and 1878 ng/L, respectively. The concentration range of detected pesticides in the Yangtze River Basin was generally lower than that in the Huai River Basin. In sediment samples, a total of 63 pesticides were detected. OPPs and amide herbicides were also ranked highest; the total concentration in sediment samples ranged from 2951 to 47,739 ng/g and 106 to 12,996 ng/g, respectively. And the mean concentrations was 6971 ng/g and 5130 ng/g, respectively. Suqian City had the highest concentration for OPPs and amide herbicides in the Huai River Basin, followed by Huai'an City, while Nanjing City and Yangzhou City ranked highest in the Yangtze River Basin. The spatial distribution of pesticides in Jiangsu Province indicated a concentration significantly higher in the western and northern regions than in the eastern and southern regions, and a concentration generally higher in lakes than in rivers. The risk assessment results showed that OPPs, fungicides, amide herbicides, organochlorines, and triazine herbicides in most surface water samples posed a high risk and had regional pollution characteristics. In sediment samples, organochlorines, carbamates, other herbicides, and other insecticides posed a high risk in northern Jiangsu Province, whereas OPPs, amide herbicides, and triazine herbicides posed high risks everywhere in Jiangsu Province.

Carbon budget and evaluation of marine industry in Jiangsu Province, China.

The measurement and evaluation of carbon budget of marine industry is the basis for promoting green and efficient development of marine economy under the goal of carbon neutrality. We constructed a carbon accounting system for the marine industry in Jiangsu Province, and assessed carbon efficiency and neutrality. The results showed that from 2016 to 2020, the total amount of marine carbon sinks in Jiangsu Province were 894.8 to 2773.2 thousand tons, while carbon emissions of major marine industries were 3538.4 to 4350.6 thousand tons. The net emissions of marine industries ranged from 1478.7 to 2906.1 thousand tons. Both of carbon sinks and emissions were significantly increased in this period. In terms of carbon sinks, the offshore wind power accounted for the largest contribution, followed by ecosystem carbon sequestration, and mariculture carbon sequestration was the smallest. In terms of carbon emissions, the marine transportation industry played a dominant role, followed by coastal tourism and marine fisheries, while the marine engineering and construction industry and marine shipping industry accounted for a small proportion. In general, the carbon neutral status showed that marine industry in Jiangsu Province was in carbon deficit from 2016-2020, but the net emissions were decreasing year by year. The net carbon sink efficiency of mariculture in Jiangsu Province was lower than the national level, and carbon efficiency of offshore wind power was stable.

[Change trends and related risk factors of disease burden on mesothelioma in Jiangsu Province from 1990 to 2019].

Objective: To analyze the change trends and risk factors of mesothelioma disease burden in Jiangsu Province from 1990 to 2019. Methods: In January 2022, using the 2019 Global Burden of Disease Study Data, the Joinpoint regression model was used to analyze the change trends of incidence, mortality, disable-adjusted life years (DALY) and premature mortality of mesothelioma residents in Jiangsu Province from 1990 to 2019, and the attribution level of mesothelioma risk factors was estimated by population attributing fraction. Results: The standardized incidence rates of mesothelioma in Jiangsu Province from 1990 to 2019 ranged from 0.07/10(5) to 0.09/10(5), with an average annual percentage change (AAPC) of -1.1% (t=-13.56, P<0.001). AAPCs in males and females were -0.3% (t=-2.18, P=0.029) and -1.6% (t=-11.39, P<0.001), respectively. The standardized mortality rates of mesothelioma ranged from 0.07/10(5) to 0.09/10(5), the AAPC was -1.1% (t=-12.23, P<0.001), AAPC was -1.6% (t=-14.09, P<0.001) for females, and there was no significant change in males (t=-1.83, P=0.068). The premature mortality was 0.004%-0.006%, the AAPC was -1.0% (t=-4.40, P<0.001), AAPC was -1.7% (t=-13.72, P<0.001) for females, and there was no significant change in males (t=-0.68, P=0.495). The standardized DALY rates ranged from 1.86/10(5) to 2.32/10(5), the AAPC was -0.9% (t=-11.08, P<0.001), AAPC was -1.6% (t=-11.05, P<0.001) for females, and there was no significant change in males (t=-0.95, P=0.343). Both the standardized years of life lost (YLL) rate and the standardized years lived with disability (YLD) rate showed a decreasing trend, and the AAPCs were -0.9% (t=-7.66, P<0.001) and -1.0% (t=-12.88, P<0.001), respectively. The proportion of YLL in DALY was more than 98.5%. Among the risk factors for mesothelioma burden attribution, the AAPC attributed to occupational asbestos exposure of DALY was 1.4% (t=3.43, P=0.001). The AAPC of DALY rate of standardized attribution was -1.7% (t=-12.11, P<0.001) . Conclusion: The overall burden of mesothelioma in Jiangsu Province is decreasing, occupational asbestos exposure is still the main risk factor of mesothelioma in Jiangsu Province, and early diagnosis and treatment should be strengthened.

[Pathway and Policy for China's Provincial Carbon Emission Peak].

An effective way for China to achieve a carbon emission peak by 2030 is to encourage developed regions to take the lead in attaining carbon peaking at the regional level. Considering Jiangsu Province as an example, this study established a provincial low emissions analysis platform (LEAP-Jiangsu) model. It combined the improved multilevel logarithmic mean Divisia index (M-LMDI) model, Tapio decoupling model, and the synergistic effect of pollution and carbon reduction model to explore the key influencing factors of carbon emissions and carbon reduction paths. The improved M-LMDI model was used to analyze the factors influencing historical and future carbon emissions in Jiangsu Province. Based on the analysis results and planning objectives, a LEAP-Jiangsu model involving various development scenarios was established to predict the time and value of carbon emission peaks. The Tapio decoupling and synergistic effect models were used to clarify the relationship between carbon emissions and economic development, the synergistic effect of carbon, and air pollutant emission reduction. The prediction results demonstrated that the total primary energy demand of Jiangsu Province in 2035 was predicted to be approximately 401.2-474.6 Mt, and the final energy demand would be approximately 319.2-382.3 Mt. Jiangsu Province was most likely to achieve the goal of carbon peaking in 2025-2030, and the peak carbon emission was approximately 815.3-845.7 Mt. The contribution rates of energy conservation and emission reduction measures such as energy intensity reduction, industrial structure optimization, terminal electrification improvement, and energy structure adjustment were 33.1%, 26.8%, 21%, and 15.2%, respectively.

Assessing the mediating role of motivation in the relationship between perceived management support and perceived job satisfaction among family doctors in Jiangsu province, China.

BACKGROUND: The study aimed to examine the influence of perceived management support on job satisfaction through the mediating role of motivation among family doctors in the Jiangsu province of China. METHODS: Six dimensions of motivation were employed as mediators in the association between perceived management support and job satisfaction in collecting data to analyze the hypothesized relationships in the present study. A total of 600 questionnaires were distributed to family doctors in Jiangsu province. Structural equation model (SEM) in the analysis of a moment structure (AMOS) version 26 software was used to estimate the path coefficients. RESULTS: Perceived management support has a significant positive relationship with job satisfaction. Motivation had a fully mediated relationship with the association between perceived management support and job satisfaction. CONCLUSIONS: The study's findings suggest motivation is important in enhancing family doctors' satisfaction and must not be underestimated. It, therefore, offers diverse recommendations for enhancing motivation among healthcare professionals.

Compensation mechanisms for fishermen quit fishing: A case of Jiangsu province, China.

The 'Ten-year Ban on Fishing' policy was designed by the Chinese government to protect the biodiversity of the Yangtze River basin. Fishermen are the ultimate implementers of the fishing ban policy. Therefore, a scientific compensation mechanism for fishers to stop fishing is the basis for ensuring the continuous implementation of the policy. First, we conducted a survey with 309 fishermen in eight cities along the Yangtze River in Jiangsu province. We also analyzed living conditions of fishermen before and after quitting fishing based on descriptive statistical analysis. Based on the theory of sustainable livelihood, a binary logistic regression model was used to analyze the relationship between fishermen's willingness to quit fishing and five types of livelihood capital (natural, material, human, financial, and social capital). The results showed that fishermen face severe livelihood sustainability issues after ceasing to fish and that their willingness to quit is closely related to the five types of livelihood capital. Based on this, and according to different age groups, this study constructed a compensation mechanism for retired fishermen from two aspects: monetary and social security compensation. The research results can provide a theoretical framework for other provinces in the Yangtze River basin to formulate a compensation system for fishermen.

Application of the Electromagnetic Method to the Spatial Distribution of Subsurface Saline and Fresh Water in the Coastal Mudflat Area of Jiangsu Province.

Interfacial zones straddling terrestrial and marine realms, colloquially known as mudflats, epitomize a dynamic nexus between these environments and are fundamental to the coastal ecosystem. The investigation of these regions is paramount for facilitating infrastructural developments including ports, wharfs, cross-sea bridges, and the strategic utilization of freshwater resources sequestered from mainland islands amid ongoing economic progress. Terrestrial realms conventionally employ electromagnetic techniques as efficacious modalities to delineate subterranean geological information, encompassing structural details and water-bearing strata. However, the peculiar topographic and geological nuances of mudflat regions pose substantial challenges for the efficacious application of electromagnetic methodologies. The present paper endeavors to address these challenges by suggesting innovative modifications to the existing instrumentation and evolving novel data acquisition techniques specifically tailored for electromagnetic exploration within mudflat environments. This paper delves into the electrical characteristics of water-bearing layers within mudflats, and ascertains details pertaining to the subterranean structure and the spatial distribution of fresh and saline water resources, through the holistic interpretation of a multitude of profiles.

[Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning].

OBJECTIVE: To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. METHODS: Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. RESULTS: A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). CONCLUSIONS: Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Heavy metal concentration profiles and pollution assessment in the Jiangsu offshore area, China.

The concentration profiles of various major and trace heavy metals (Cu, Pb, Zn, Cr, Cd, As, Hg, Ni, Li, and Co) were investigated along a 40.1-m-long sediment core in the offshore Jiangsu area of China, to assess their depositional trends and contamination levels. All metals, except Cd, exhibited similar profiles with high average concentrations during the Marine Isotope Stage 2-4 period. The sediment trace-metal concentrations were primarily related to grain size and sediment sources, with almost all heavy metals (except Cd) being positively correlated. Enrichment factors, geoaccumulation indices, and principal component analysis indicated no elemental enrichment or contamination. The high EF and Igeo values of As, Hg, and Li may be related to their background values.

Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model.

Recently, the global background concentration of ozone (O3) has demonstrated a rising trend. Among various methods, groun-based monitoring of O3 concentrations is highly reliable for research analysis. To obtain information on the spatial characteristics of O3 concentrations, it is necessary that the ground monitoring sites be constructed in sufficient density. In recent years, many researchers have used machine learning models to estimate surface O3 concentrations, which cannot fully provide the spatial and temporal information contained in a sample dataset. To solve this problem, the current study utilized a deep learning model called the Residual connection Convolutional Long Short-Term Memory network (R-ConvLSTM) to estimate daily maximum 8-hr average (MDA8) O3 over Jiangsu province, China during 2020. In this research, the R-ConvLSTM model not only provides the spatiotemporal information of MDA8 O3, but also involves residual connection to avoid the problem of gradient explosion and gradient disappearance with the deepening of network layers. We utilized the TROPOMI total O3 column retrieved from Sentinel-5 Precursor, ERA5 reanalysis meteorological data, and other supplementary data to build a pre-trained dataset. The R-ConvLSTM model achieved an overall sample-base cross-validation (CV) R2 of 0.955 with root mean square error (RMSE) of 9.372 µg/m3. Model estimation also showed a city-based CV R2 of 0.896 with RMSE of 14.029 µg/m3, the highest MDA8 O3 in spring being 122.60 ± 31.60 µg/m3 and the lowest in winter being 69.93 ± 18.48 µg/m3.

How does digital governance affect the level of domestic waste separation for rural residents? Empirical evidence from rural areas in Jiangsu Province, China.

Introduction: The management of rural domestic waste is directly related to the quality of China's rural habitat and the ecological security of the countryside, and is one of the important tasks of rural revitalization. Methods: Based on the perspective of digital technology empowering rural governance, this study uses the China Land Economic Survey (CLES) data to empirically test the impact of digital governance on the level of domestic waste separation for rural residents by constructing the ordered probit model. Results and discussion: The results show that in the process of rural governance modernization, digital governance helps to improve the level of domestic waste separation for rural residents in the process of rural governance modernization, and the findings still hold after robustness tests. Mechanistic tests showed that digital governance can impact the level of domestic waste separation for rural residents through cadre-mass relationship and institutional trust. The findings of this study provide a new perspective on good environmental governance in China's countryside and have important implications for promoting the improvement of rural habitat quality.