ORCID Profile
0000-0003-1873-3125
Current Organisation
Zhejiang University
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Publisher: Elsevier BV
Date: 12-2020
Publisher: Frontiers Media SA
Date: 16-11-2022
DOI: 10.3389/FPUBH.2022.1034534
Abstract: Most studies have focused on the relationship between ambient temperature and stroke mortality, but studies on the relationship between ambient temperature and stroke occurrence are still limited and inconsistent. This study aimed to analyze the effect of ambient temperature and other environmental factors on emergency stroke visits in Beijing. Our study utilized stroke visit data from the Beijing Red Cross Emergency Medical Center during 2017–2018, and applied a generalized additive model (GAM) as well as a distributed lag non-linear model (DLNM), respectively, regarding the direct, lagged, and cumulative effects of ambient temperature alone and with correction for other environmental factors on stroke occurrence. With a total of 26,984 emergency stroke patients in 2017–2018, both cold and hot effects were observed and weakened after correction for other environmental factors. Compared to the reference temperature, in the multi-factor model, extreme cold (−10°C) reached a maximum relative risk (RR) of 1.20 [95% Confidence Interval (CI): 1.09, 1.32] at lag 14 days, and extreme hot (30°C) had a maximum RR of 1.07 (95% CI: 1.04, 1.11) at lag 6 days. The cumulative effect of extreme cold reached a maximum of 2.02 (95% CI: 1.11, 3.67) at lag 0–14 days, whereas the cumulative effect of extreme hot temperature is greatest at lag 0–10 days, but no statistically significant effect was found. In addition, ischemic stroke patients, the elderly, and males were more susceptible to the effects of cold temperature. There is a non-linear relationship between ambient temperature and stroke occurrence, with cold temperature having a greater and longer-lasting impact than hot temperature.
Publisher: MDPI AG
Date: 16-09-2019
Abstract: Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses. About 90% of global cases were reported in China. We collected monthly data on counts of HFRS cases, climatic factors (mean temperature, rainfall, and relative humidity), and vegetation (normalized difference vegetation index (NDVI)) in 109 Chinese counties from January 2002 to December 2013. First, we used a quasi-Poisson regression with a distributed lag non-linear model to assess the impacts of these four factors on HFRS in 109 counties, separately. Then we conducted a multivariate meta-analysis to pool the results at the national level. The results of our study showed that there were non-linear associations between the four factors and HFRS. Specifically, the highest risks of HFRS occurred at the 45th, 30th, 20th, and 80th percentiles (with mean and standard deviations of 10.58 ± 4.52 °C, 18.81 ± 17.82 mm, 58.61 ± 6.33%, 198.20 ± 22.23 at the 109 counties, respectively) of mean temperature, rainfall, relative humidity, and NDVI, respectively. HFRS case estimates were most sensitive to mean temperature amongst the four factors, and the lag patterns of the impacts of these factors on HFRS were heterogeneous. Our findings provide rigorous scientific support to current HFRS monitoring and the development of early warning systems.
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 05-2019
DOI: 10.1016/J.WATRES.2019.01.056
Abstract: We are still facing the knowledge gap of how the water-quality extremes (i.e. phytoplankton blooms), their causes, severity or occurrence could be directly related to the climatic oscillation. Considering that the climatic and phytoplankton concentration time series are highly non-stationary, we applied the advanced time-frequency analysis - Ensemble Empirical Mode Decomposition (EEMD), Hilbert-Huang Spectrum (HHS) and Wavelet Analysis (WA) - to examine the variability of long term phytoplankton dynamics from 1986 to 2014 in five North Temperate Lakes (NTLs). These analysis techniques isolated five separate time series for the surface Chlorophyll a concentrations (CHL) of the five NTLs and a time series for the global climate oscillation (denoted by multivariate ENSO index, MEI), and showed that these time series generally operated at similar time scales. The long-term residual trends of decreasing were found in three lakes (i.e., BM, SP and TR lakes), which are the same to global climate dynamics (MEI). The wavelet analysis reveals strong coherency between MEI and CHL data sets for all lakes, with a periodicity of 64-months. Intuitive associations between the CHL and MEI data set showed that two types of ENSO (El Nino and La Nina) differ in their influences to CHL. Potential mechanisms relating the phytoplankton dynamics in NTLs to climatic oscillation (ENSO) were also discussed.
Publisher: MDPI AG
Date: 25-05-2021
DOI: 10.3390/FRACTALFRACT5020049
Abstract: Phytoplankton movement patterns and swimming behavior are important and basic topics in aquatic biology. Heavy tail distribution exists in erse taxa and shows theoretical advantages in environments. The fat tails in the movement patterns and swimming behavior of phytoplankton in response to the food supply were studied. The log-normal distribution was used for fitting the probability density values of the movement data of Oxyrrhis marina. Results showed that obvious fat tails exist in the movement patterns of O. marina without and with positive stimulations of food supply. The algal cells tended to show a more chaotic and disorderly movement, with shorter and neat steps after adding the food source. At the same time, the randomness of turning rate, path curvature and swimming speed increased in O. marina cells with food supply. Generally, the responses of phytoplankton movement were stronger when supplied with direct prey cells rather than the cell-free filtrate. The scale-free random movements are considered to benefit the adaption of the entire phytoplankton population to varied environmental conditions. Inferentially, the movement pattern of O. marina should also have the characteristics of long-range dependence, local self-similarity and a system of fractional order.
Publisher: IOP Publishing
Date: 2023
Abstract: The timeliness of monitoring is essential to algal bloom management. However, acquiring algal bio-indicators can be time-consuming and laborious, and bloom biomass data often contain a large proportion of extreme values limiting the predictive models. Therefore, to predict algal blooms from readily water quality parameters (i.e. dissolved oxygen, pH, etc), and to provide a novel solution to the modeling challenges raised by the extremely distributed biomass data, a Bayesian scale-mixture of skew-normal (SMSN) model was proposed. In this study, our SMSN model accurately predicted over-dispersed biomass variations with skewed distributions in both rivers and lakes (in-s le and out-of-s le prediction R 2 ranged from 0.533 to 0.706 and 0.412 to 0.742, respectively). Moreover, we successfully achieve a probabilistic assessment of algal blooms with the Bayesian framework (accuracy .77 and macro- F 1 score .72), which robustly decreased the classic point-prediction-based inaccuracy by up to 34%. This work presented a promising Bayesian SMSN modeling technique, allowing for real-time prediction of algal biomass variations and in-situ probabilistic assessment of algal bloom.
Publisher: Elsevier BV
Date: 04-2022
DOI: 10.1016/J.SCITOTENV.2021.152887
Abstract: Sediments of blue carbon vegetation are becoming a sink of natural and anthropogenic pollutants, such as polycyclic aromatic hydrocarbons (PAHs). However, the extent to which PAHs are accumulated and varied in blue carbon sediments, and the impact of blue carbon vegetation on the accumulation and retention capacity of PAHs, have been poorly explored. This study examines the sedimentary records of PAHs in profiles from mangrove plantation, saltmarsh, and mudflat in Ximen Island and Maoyan Island of Yueqing Bay, China. The existence of blue carbon vegetation provides a sheltered environment for the accelerated burial of sediment and OC. Decadal PAH sedimentation records show staged changes characterized by the emission of PAHs and colonization of blue carbon vegetation, reflecting the accelerated burial of PAHs in sediments by blue carbon vegetation colonization. In addition, the colonization of blue carbon vegetation contributes to the shift of PAH compositions in sediments. This study provides new insights into the underestimated PAH accumulation potential and retention capacity of blue carbon vegetation and the corresponding underlying sediments, supporting the environmental benefits of blue carbon vegetation.
Publisher: Informa UK Limited
Date: 08-05-2022
Publisher: Frontiers Media SA
Date: 12-01-2022
DOI: 10.3389/FENVS.2021.783864
Abstract: Scrub typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modeling approaches at forecasting ST cases during 2012–2020 in eight high-risk counties in China. We evaluated the performance of a seasonal autoregressive-integrated moving average (SARIMA) model, a SARIMA model with exogenous variables (SARIMAX), and the long–short term memory (LSTM) model to depict temporal variations in ST cases. In our investigation, we considered eight environmental variables known to be associated with ST landscape epidemiology, including the normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, and multivariate El Niño/Southern Oscillation index (MEI). The first 8-year data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, suggesting that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high-risk areas under investigation. Furthermore, the introduction of time-series models, especially LSTM, has enriched the ability of local public health authorities in ST high-risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system and forecasting ST precisely.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 12-2022
Publisher: Informa UK Limited
Date: 24-04-2023
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.ENVRES.2022.114581
Abstract: Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing. The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB. Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 μg/m Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.WATRES.2022.118591
Abstract: The rapid emergence of deep learning long-short-term-memory (LSTM) technique presents a promising solution to algal bloom forecasting. However, the discontinuous and non-stationary processes within algal dynamics still largely limit the functions of LSTMs. To overcome this challenge, an advanced time-frequency wavelet analysis (WA) technique was introduced to enhance the prediction accuracy of LSTMs. Herein, the novel hybrid approach (named WLSTM) successfully decreased the algal forecasting inaccuracy of classic LSTMs by 41% ± 8% in Lake Mendota (Wisconsin, USA), with powerful one-step-ahead predictions at hourly, daily, and monthly time resolutions (R
Publisher: Elsevier BV
Date: 04-2023
No related grants have been discovered for Junyu He.