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Assessing eutrophication in lakes is of key importance, as this parameter constitutes a major aquatic ecosystem integrity indicator. The trophic state index (TSI), which is widely used to quantify eutrophication, is a universal paradigm in the scientific literature. In this study, a methodological framework is proposed for quantifying and mapping TSI using the Sentinel Multispectral Imager sensor and fieldwork samples. The first step of the methodology involves the implementation of stepwise multiple regression analysis of the available TSI dataset to find some band ratios, such as blue/red , green/red and red/red , which are sensitive to lake TSI. Trained with in situ measured TSI and match-up Sentinel images, we established the XGBoost of machine learning approaches to estimate TSI, with good agreement (R2= 0.87, slope = 0.85) and fewer errors (MAE = 3.15 and RMSE = 4.11). Additionally, we discussed the transferability and applications of XGBoost in three lake classifications: water quality, absorption contribution and reflectance spectra types. We selected XGBoost to map TSI in 2019–2020 with good-quality Sentinel-2 Level-1C images embedded in the ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10 m TSI products from 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate and anthropogenic activities were investigated. The methodological framework proposed herein could serve as a useful resource for continuous, long-term and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management. [ABSTRACT FROM AUTHOR]

This study reveals that the relationship between autumn north tropical Atlantic (NTA) sea surface temperature (SST) anomalies and Northeast China's winter snowfall (NECWS) undergoes a remarkable interdecadal enhancement after 2001. Previous research confirmed that the relationship between the NTA SST anomaly and atmospheric circulation experienced interdecadal changes after the 2000s and suggested various reasons for this phenomenon. During 1961–2000, the NTA SST anomaly has a significantly positive correlation with other oceans, especially the tropical Indian Ocean (TIO), and the latter modulates the former's impact on atmospheric circulations over the Eurasian continent with a cancelling effect, which results in a weaker relationship of the NTA SST anomaly and NECWS. In contrast, the warm NTA SST anomaly is relatively independent from other oceans during 2001–20, and it proves to be the forcing factor for NECWS since its solo influence on the winter atmospheric circulations initiated from the North Atlantic to East Asia is more robust, featuring the negative phase of the North Atlantic Oscillation and a downstream quasi-barotropic Rossby wave train over the mid- to high latitudes of Eurasian continent. Accordingly, together with the deepened East Asian trough and the strongly northward transported humid and warm air from the western Pacific, the local significantly enhanced ascending motions with cooling temperature favor much more NECWS. The linear baroclinic model simulates the effects of NTA and TIO SST anomalies on winter atmospheric circulations, corroborating the aforementioned results. Such results can be used for the prediction of NECWS with respect to the precursor of the autumn NTA SST anomaly. [ABSTRACT FROM AUTHOR]

Based on the daily minimum temperature data from 199 meteorological stations in northeast China (NEC), the cold wave (CW) processes in winter from 1980 to 2020 are identified. Then, by using the reanalysis 500‐hPa geopotential height data provided by the European Centre for Medium‐Range Weather Forecasts (ECMWF), the CWs are objectively classified into two types (abbreviated as Type 1 and Type 2) according to the corresponding atmospheric circulation through the hierarchical cluster algorithm (HCA). The relationships of the CWs with the Arctic sea sic (SIC) and sea surface temperature (SST) are revealed. For Type 1, the SIC in the north of the Barents Sea–Kara Sea is relatively less in the previous autumn, and a wave train propagating from the polar regions to Northeast Asia is stimulated under the ice–atmosphere interaction. This wave train further cooperates with the positive SST anomaly in the southern Indian Ocean to excite the low‐vortex circulation under the negative phase of Arctic Oscillation (AO) in winter, which is conducive to the occurrence of Type 1. However, the relatively low SST in the western Pacific warm pool in previous autumn stimulates a northward propagating wave train along the meridian, which increases the degree of meridionality of the atmospheric circulation over East Asia and thus indirectly causes the polar‐vortex‐splitting circulation under the positive AO phase, which favours the formation of Type 2. The numerical simulations can well reproduce the observational facts, further confirming the possible impact of the SIC and SST in the previous autumn on the two types of CW over the NEC. [ABSTRACT FROM AUTHOR]

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Li, Y.; Wang, Y.; Xu, S.; Hu, B., and Wang, Z.-L., 2017. Effects of mariculture and solar-salt production on sediment microbial community structure in a coastal wetland. Mariculture and solar-salt production are two pervasive anthropogenic activities in worldwide coastal areas; however, their effects on sediment microbial biomass, community composition, and diversity have received less attention. Here, this question was investigated using the phospholipid fatty acids (PLFAs) analysis and 16S rRNA gene sequencing in Bohai Rim, northern China. Both mariculture and salt production increased bacterial (+135% and +84%), fungal (+45% and +20%), and total PLFAs contents (+72% and +39%) compared with intertidal wetlands. Furthermore, mariculture and salt production shifted microbial PLFAs compositions. The ratio of fungi:bacteria-PLFAs decreased in mariculture ponds (−40%) and salt fields (−37%) relative to the undisturbed wetland, and the ratio of Gram-positive:Gram-negative bacteria decreased in the salt fields (−67%). Mariculture promoted the relative abundances of Firmicutes and Gemmatimonadetes, while salt production stimulated the relative abundances of Actinobacteria, Spirochaetes, Tenericutes, and Chlamydiae, as compared with the intertidal wetland. The changes in the microbial community composition were mainly attributed to sediment organic carbon, dissolved organic carbon, total nitrogen, and . The microbial and bacterial Shannon-Wiener indices, however, did not change under mariculture and salt production. In conclusion, mariculture and sea-salt production had a broad range of effects on sediment microbial biomass and community composition but had little effect on diversity. [ABSTRACT FROM AUTHOR]

• Red/Red edge and Blue/Red combinations can qualify trophic state index. • The > 77% of investigated eastern lakes (>100 km2) are eutrophic. • The eutrophication would not be improved before- and after- COVID-19 lockdown. Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N = 431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = –34.04 × (band 4/band 5) – 1.114 × (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE = 7.36; MAE = 6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (>100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (>77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as "autochthonous sources" of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas. [ABSTRACT FROM AUTHOR]

New particle formation (NPF) events are an increasingly interesting topic in air quality and climate science. In this study, the particle number size distributions, and the frequency of NPF events over Hefei were investigated from November 2018 to February 2019. The proportions of the nucleation mode, Aitken mode, and accumulation mode were 24.59%, 53.10%, and 22.30%, respectively, which indicates the presence of abundant ultrafine particles in Hefei. Forty-six NPF events occurred during the observation days, accounting for 41.82% of the entire observation period. Moreover, the favorable meteorological conditions, potential precursor gases, and PM 2.5 range of the NPF events were analyzed. Compared to non-NPF days, the NPF events preferentially occurred on days with lower relative humidity, higher wind speeds, and higher temperatures. When the PM 2.5 was 15-20, 70-80, and 105-115 μg/m 3 , the frequency of the NPF events was higher. Nucleation mode particles were positively related to atmospheric oxidation indicated by ozone when PM 2.5 ranged from 15 to 20 μg/m 3 , and related to gaseous precursors like SO 2 and NO 2 when PM 2.5 was located at 70-80 and 105-115 μg/m 3 . On pollution days, NPF events did not directly contribute to the increase in the PM 2.5 in the daytime, however, NPF events would occur during the night and the growth of particulate matter contributes to the nighttime PM 2.5 contents. This could lead to pollution that lasted into the next day. These findings are significant to the improvement of our understanding of the effects of aerosols on air quality., (Copyright © 2021. Published by Elsevier B.V.)

To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 - Apr. 8, 2020), PM 2.5 , PM 10 , NO 2 , SO 2 , and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015-2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NO x at least 43 %. However, O 3 increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 - Feb. 14, 2020), which likely reduced the production of O 3 , O 3 concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NO x reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O 3 concentrations, even if reaching the limit, and VOC-specific measures should also be taken., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Studying the characteristics of new particle formation (NPF) is conducive to exploring the impact of atmospheric particulate matter on the climate, environment, and human health. The particle number size distributions (5.6-560 nm) of aerosols were measured using a fast mobility particle sizer (FMPS) from 1 to 11 May 2019. The clean atmosphere was one of the basic conditions for the occurrence of this continuous new particle formation events. It started between 9:00 and 12:00, and it mainly ended after 20:00. The growth rate (GR) and condensation sink (CS) values in Hefei were 2.98 ± 0.97 nm·h -1 and (3.0 ± 0.4) × 10 -2 s -1 , respectively. Back trajectory clustering analysis revealed that the mass concentration of the air masses from the southeastern part of Henan Province and the southern part of Anhui Province surrounding the study area were relatively high. The analysis results of the potential source contribution function (PSCF) and the concentration weighted trajectory (CWT) methods show that in addition to local pollution, the long-distance transport of pollutants in the Yangtze River Delta (YRD) greatly contributed to the accumulation modal particulate concentration in Hefei. Moreover, the population affected by PM 2.5 during the observation period reached 8.19 × 10 4 , accounting for 1.08% to the total population in Hefei. The premature death cases associated with PM 2.5 reached 8.35 × 10 2 . This study is helpful to understand the main influencing factors of consecutive NPF events and the health risks of fine particles., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-a concentrations. In this study, a total of 273 samples were collected from 45 typical lakes across China during 2017-2019. Here, we proposed applicable machine learning algorithms (i.e., linear regression model (LR), support vector machine model (SVM) and Catboost model (CB)), which integrate a broad scale dataset of lake biogeochemical characteristics using Multispectral Imager (MSI) product to seamlessly retrieve the Chl-a concentration. A K-means clustering approach was used to cluster the 273 normalized water leaving reflectance spectra [Rrs (λ)] extracted from MSI imagery with Case 2 Regional Coast Colour (CR2CC) processor into three groups. The pH, electrical conductivity (EC), total suspended matter (TSM) and dissolved organic carbon (DOC) from three clustering groups had significant differences (p < 0.05**), indicating that water quality parameters have an integrated impact on Rrs(λ)-spectra. The results of machine learning algorithms integrating demonstrated that SVM obtained a better degree of measured- and derived- fitting (calibration: slope = 0.81, R 2  = 0.91; validation: slope = 1.21, R 2  = 0.88). On the contrary, the documented nine Chl-a algorithms gave poor results (fitting 1:1 linear slope < 0.4 and R 2  < 0.70) with synchronous train and test datasets. It demonstrated that machine learning provides a robust model for quantifying Chl-a concentration. Further, considering three Rrs(λ) clustering groups by k-means, Chl-a SVM model indicated that cluster 1 group gave a better retrieving performance (slope = 0.71, R 2  = 0.78), followed by cluster 3 group (slope = 0.77, R 2  = 0.64) and cluster 2 group (slope = 0.67, R 2  = 0.50). These are related to the low TSM and high DOC levels for cluster-1 and cluster-3 Rrs(λ) spectra, which reduce the influence of particle in red bands for Rrs(λ) signal. Our results highlighted the quantification of lake Chl-a concentrations using MSI imagery and SVM, which can realize the large-scale monitoring and more appropriate for medium/low Chl-a level. The remote estimation of Chl-a based on artificial intelligence can provide an effective and robust way to monitor the lake eutrophication on a macro-scale; and offer a better approach to elucidate the response of lake ecosystems to global change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)