ORCID Profile
0000-0002-5431-9274
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Publisher: Elsevier BV
Date: 09-2022
DOI: 10.1016/J.SCITOTENV.2022.156055
Abstract: Harmful algal blooms (HAB) are a serious problem worldwide. Allelochemicals from natural plants were recently thought to be promising anti-algaecide in controlling harmful algae. However, the programmed cell death (PCD) process of algae under allelopathic pressure induced by 5,4'-dihydroxyflavone (5,4'-DHF) was poorly understood. In this study, two common and worldwide distributed microalgae, Microcystis aeruginosa and Phaeocystis globosa were selected as target algae, and the PCD processes induced by 5,4'-DHF were cross-compared between the two species. Both algae species were inhibited significantly by 5,4'-DHF with the relative sensitivity of 0.11. To uncover the PCD progress systematically, signals for PCD triggering, antioxidant enzyme activity, photosynthetic ability variation, caspase-like activities and typical indicators were investigated. In both species, typical indicators of PCD - phosphatidylserine externalization and chromatin condensation - were detected. The intracellular reactive oxygen species (ROS), nitric oxide (NO) and H
Publisher: Elsevier BV
Date: 02-2020
DOI: 10.1016/J.ENVPOL.2019.113450
Abstract: Microplastics is an emerging environmental problem in the world. However, presence and fate of microplastics in seagrass meadows are barely known. In this study, the abundance and ersity of microplastic from Enhalus acodoides vegetated sites and bare sites were quantified and characterized in Xincun bay and Li'an bay, Hainan, China. Microplastics ranged from 80.0 to 884.5 particles per kg of dry sediment, and fibers were the dominant shape. The most frequent colors of microplastics were blue, transparent and black. The dominant size of microplastics was in the range of 125-250 μm. And the seagrass sediments were enriched in microplastics 2.1 and 2.9 times for Xincun bay and Li'an bay, respectively. The trap effect of seagrass was non-selective regarding the shape, color and size of microplastics. High anthropogenic pollution and poor beach management may contribute to higher concentrations of microplastics in Li'an bay.
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2020
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 12-2022
Publisher: MDPI AG
Date: 03-09-2022
Abstract: Assessing the impacts of both climate and land use changes on hydrologic variables is crucial for sustainable development of water resources and natural ecosystems. We conducted a case study of a catchment in southwestern Australia to assess the impacts of future climate and land use changes, both separately and in combination, on water resource availability. For this evaluation, the Soil and Water Assessment Tool (SWAT) model was first calibrated and then forced by 34 global climate models (GCMs), under two Representative Concentration Pathways (RCP4.5 and RCP8.5) and five land use scenarios (LU0–4). Our results suggested that SWAT reproduced the observed monthly streamflow well. Land use changes have impacts on all hydrologic variables, especially on runoff at the annual scale. Future runoff was projected to decrease in all seasons, especially winter and spring. For the combined effects of climate and land use changes, the results of LU1–4 were only slightly different from the response of LU0. An uncertainty analysis shows that GCMs had the greatest contribution to hydrologic variables, followed by RCPs and land use scenarios. Hence, it is advisable for impacts analysis to use an ensemble of GCMs under different RCPs to minimize the uncertainty of projected future hydrologic variables.
Publisher: Wiley
Date: 03-07-2023
Abstract: Harmful algal blooms (HABs), which are promoted by eutrophication and intensified by global warming, occur worldwide. Allelochemicals, which are natural chemicals derived from plants or microbes, are emerging weapons to eliminate these blooms. However, the cost and technical challenges have limited the discovery of novel antialgal allelochemicals. Herein, the decomposition of agricultural straws is manipulated by white‐rot fungi and achieved elevated antialgal efficiency. The transcriptomic analysis reveals that nutrient limitation activated fungal decomposition. By using a comparative nontarget metabolomics approach, a new type of allelochemical sphingosines (including sphinganine, phytosphingosine, sphingosine, and N ‐acetylsphingosine) is identified. These novel natural algaecides exhibit superior antialgal capability, with as high as an order of magnitude lower effective concentration on blooming species than other prevalent allelochemicals. The co‐expression relationship between transcriptomic and metabolomic results indicate that sphinganine is strongly correlated with the differentially expressed lignocellulose degradation unigenes. The algal growth suppression is triggered by the activation of programmed cell death, malfunction of algal photosystem and antioxidant system, the disruption on CO 2 assimilation and light absorption. The sphingosines reported here are a new category of allelochemicals in addition to the well‐known antialgal natural chemicals, which are potential species‐specific agents for HABs control identified by multi‐omics methodology.
Publisher: Wiley
Date: 05-03-2020
DOI: 10.1002/LDR.3577
Abstract: The cover‐management factor (C‐factor) is used in the revised universal soil loss equation to represent the effect of vegetation cover and its management practices on hillslope erosion. Remote sensing has been widely used to estimate vegetation cover and the C‐factor, but most previous studies only used the photosynthetic vegetation (PV) or green vegetation indices (VI, e.g., normalized difference VI) for estimating the C‐factor and the important non‐PV (NPV) component was often ignored. In this study, we developed a new technique to estimate monthly time‐series C‐factor using the fractional vegetation cover (FVC) including both PV and NPV, and weighted by monthly rainfall erosivity ratio. The monthly FVC was derived from the moderate resolution imaging spectroradiometer and LANDSAT data with field validation. We conducted the case‐study over China's Loess Plateau and analysed the spatiotemporal variations of FVC and the C‐factor and their impacts on erosion over the Plateau. Our study reveals a significant increase in total vegetation cover (TC) from 56 to 76.8%, with a mean of 71.2%, resulting in about 20% decrease in the C‐factor and erosion risk during the 17‐year period. Our method has an advantage in estimating the C‐factor from TC at a monthly scale providing a basis for continuously and consistently monitoring of vegetation cover, erosion risk and climate impacts.
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: 05-2022
DOI: 10.1016/J.MARPOLBUL.2022.113657
Abstract: Harmful algal blooms (HABs) have induced severe damage worldwide. A novel high-efficient antialgal natural chemical, 3-indoleacrylic acid (3-IDC) with a 5-day half-maximal inhibitory concentration (IC
Publisher: Wiley
Date: 02-01-2020
DOI: 10.1111/TGIS.12607
Publisher: Elsevier BV
Date: 07-2021
Publisher: Elsevier BV
Date: 04-2020
DOI: 10.1016/J.SCITOTENV.2020.136737
Abstract: This study investigated the response of three toxic harmful algal blooms (HABs) species, Heterosigma akashiwo, Chattonella marina, and Alexandrium tamarense to coastal invasive plant, Spartina alterniflora. In this study, the growth of three HABs species were suppressed significantly by S. alterniflora extracts, showing a dose-response relationship. The raphidophyte H. akashiwo and C. marina exhibited higher inhibitory response with EC
Publisher: American Chemical Society (ACS)
Date: 11-03-2021
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: MDPI AG
Date: 20-11-2020
DOI: 10.3390/RS12223805
Abstract: The Australian Black Summer wildfires between September 2019 and January 2020 burnt many parts of eastern Australia including major forests within the Sydney drinking water catchment (SDWC) area, almost 16.000 km2. There was great concern on post-fire erosion and water quality hazards to Sydney’s drinking water supply, especially after the heavy rainfall events in February 2020. We developed a rapid and innovative approach to estimate post-fire hillslope erosion using weather radar, remote sensing, Google Earth Engine (GEE), Geographical Information Systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE). The event-based rainfall erosivity was estimated from radar-derived rainfall accumulations for all storm events after the wildfires. Satellite data including Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) were used to estimate the fractional vegetation covers and the RUSLE cover-management factor. The study reveals that the average post-fire erosion rate over SDWC in February 2020 was 4.9 Mg ha−1 month−1, about 30 times higher than the pre-fire erosion and 10 times higher than the average erosion rate at the same period because of the intense storm events and rainfall erosivity with a return period over 40 years. The high post-fire erosion risk areas (up to 23.8 Mg ha−1 month−1) were at sub-catchments near Warragamba Dam which forms Lake Burragorang and supplies drinking water to more than four million people in Sydney. These findings assist in the timely assessment of post-fire erosion and water quality risks and help develop cost-effective fire incident management and mitigation actions for such an area with both significant ecological and drinking water assets. The methodology developed from this study is potentially applicable elsewhere for similar studies as the input datasets (satellite and radar data) and computing platforms (GEE, GIS) are available and accessible worldwide.
Publisher: Elsevier BV
Date: 04-2023
No related grants have been discovered for mingxi zhang.