Key factors affecting NH3-N in the Huaihe River Basin due to human activities.

Human activity factors have a significant impact on changes in ammonia nitrogen (NH3-N) content in rivers. Existing research mainly focuses on human activity factors as type factors, and lacks research on the key factors affecting river NH3-N among human activity factors. Therefore, this paper aims to study the key factors affecting human activities on NH3-N in the Huaihe River through various statistical analysis methods. The study found that changes in NH3-N content in the Huaihe River are mainly affected by land use patterns in the basin. There are two different ways in which land use affects NH3-N in rivers: direct effects and indirect effects. We also studied the main pathways through which changes in key factors in human activities affect NH3-N in the Huaihe River by constructing a structural equation model. The results showed that crop sowing area and afforestation area have a significant direct effect on NH3-N in the Huaihe River. In addition, crop sowing area and afforestation area can also affect river NH3-N by regulating the amount of nitrogen fertilizer and human excrement. This study is of great significance for understanding how human activities regulate NH3-N content in rivers.

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China.

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.

Exploring the supply and demand imbalance of carbon and carbon-related ecosystem services for dual­carbon goal ecological management in the Huaihe River Ecological Economic Belt.

The measurement of carbon and carbon-related ecosystem services (CCESs) has garnered considerable global attention, primarily due to dual­carbon goals, which are crucial for the rational allocating of ecosystem service (ES) resources and the enhancement of terrestrial carbon sinks. This study developed a novel research framework on CCESs to quantitatively measure carbon storage (CS), food production (FS), habitat quality (HQ), soil conservation (SC), and water yield (WY), and examined the spatiotemporal patterns of the supply-demand and trade-off/synergy processes related to CCESs in the Huaihe River Ecological Economic Belt (HREEB). The findings are as follows: (1) From 2000 to 2020, the supply-demand of the CCESs generally increased, except for carbon storage and food demand. Overall, the supply level of the CCESs exceeds the demand level, with a median ratio of supply and demand ratio (ESDR) of 1.13. (2) During the study period, the synergy relationship of the CCESs is mainly determined by the supply side of the CS-HQ and CS-SC, while on the demand side, it is determined by the CD- FD. And the ESDR of all C-related ecosystem services showed a significant synergy strengthening with CS in the HREEB. (3) Spatially, "high-low" spatial matching of the ESDR decreased, suggesting a gradual reduction in the spatial mismatch of CCESs. (4) We identified seven ecological functional zones and proposed corresponding strategies for promoting ecological management. Our research emphasized the spatiotemporal patterns of supply and demand imbalance in CCESs and the spatial optimization paths of trade-offs/synergies, providing valuable insights for achieving regional dual­carbon goals.

[Temporal-spatial distribution of functional feeding groups of macroinvertebrate and biological evaluation of water quality in Xinyang section of the Huaihe River main stream]. / æ·®æ²³å¹²æµä¿¡é˜³æ®µå¤§åž‹åº•æ –åŠ¨ç‰©åŠŸèƒ½æ‘„é£Ÿç±»ç¾¤æ—¶ç©ºåˆ†å¸ƒåŠæ°´è´¨è¯„ä»·.

From November 2021 to September 2022, we conducted four field surveys on macroinvertebrates and water environmental factors in Xinyang section of the Huaihe River main stream. We analyzed the structure and spatiotemporal distribution characteristics of the functional feeding groups of macroinvertebrates, and evaluated river water quality. A total of 73 macroinvertebrate species were collected in the basin, belonging to 42 families, 7 classes, and 3 phyla. The dominant species of macroinvertebrates changed significantly in different months, with Exopalaemon modestus being the absolute advantage species in the basin in July and September 2022. In different sampling months, the functional feeding group of macroinvertebrates was mainly dominated by shredders, accounting for 35.9%. The results of redundancy analysis showed that the main environmental factors affecting the distribution of functional feeding groups of macroinvertebrates varied across different months, with conductivity in February, temperature in July, and oxidation-reduction potential in September and November. The evaluation based on the biolo-gical index and Shannon index of macroinvertebrates indicated that water quality in the investigated section was at a light pollution level.

[Occurrence Characteristics and Risk Assessment of Microplastics in Water and Sediments of Anhui Section of Huaihe River Basin].

The occurrence characteristics and ecological risk level of microplastics in the water and sediments of the Anhui section of Huaihe River Basin were analyzed via field sampling, stereomicroscopy, scanning electron microscopy, Fourier transform infrared spectroscopy (FTIR), risk index (H), and pollution load index (PLI) model. The current situation of microplastics in the water and sediments of the basin was analyzed, and the ecological risk assessment of microplastics was conducted. The results showed that the detection rate of microplastics at each site in the basin was 100%. The average abundance of microplastics in surface water and sediments was (39800±3367) n·m-3 and (5078±447) n·kg-1, respectively. The average abundance of microplastics in the downstream was higher than that in the upstream and midstream. The particle size of microplastics in water and sediments was primarily 20-150 µm, accounting for 82.96% and 80.77%, respectively. The microplastics were primarily fiber (water 76.05%, sediment 84.53%), film (water 21.83%, sediment 15.43%), and debris (water 2.12%, sediment 0.04%). The microplastics in water and sediments were primarily transparent, accounting for 63.31% and 83.69%, respectively. Polyethylene (65.74% in water and 80.62% in sediment) and polypropylene (18.43% in water and 9.71% in sediment) were the major components of water and sediments. Microplastics were primarily derived from agricultural films, abandoned fishing gear and nets, and artificially abandoned plastic bags in ports. The microplastic risk index (H) model assessment revealed that the risk index of some sites was high, and the risk level of microplastics in the Anhui section of Huaihe River Basin was grade II. The pollution load index (PLI) model assessment revealed that the ecological risk of surface water and sediments in the basin was generally low.

Application of the multimedia fugacity model in predicting the environmental behaviors of PCBs: Based on field measurements and level III fugacity model simulation.

The comprehensive understanding of PCBs' fate has been impeded by the lack of simultaneous monitoring of PCBs in multiple environmental media in the background areas, which were considered long-term sinks for highly chlorinated PCBs. To address this gap, this study analyzed soils, willow tree barks, water, suspended particulate matter (SPM), and sediment samples collected from the middle reach of the Huaihe River in China for 27 PCBs. The results showed that the levels of ∑27PCBs in the soils were comparable to or lower than the background values worldwide. There were no significant correlations between organic matter and ∑27PCB concentrations in the soils and sediments. Additionally, the contamination of dioxin-like PCBs in the aquatic environment of the study area deserves more attention than in the soils. Applying the level III fugacity model to PCB 52, 77, 101, and 114 revealed that the soil was the primary reservoir, and air-soil exchange was the dominant intermedia transfer process, followed by air-water exchange. Furthermore, simulated results of air-soil and air-water diffusion were compared with those calculated from the field concentrations to predict the potential environmental behaviors of PCBs. Results indicated that the studied river would be a "secondary source" for PCB 52, 77, and 101. However, PCB 52, 77, 101, and 114 would continue to transfer from the air to the soil. This study combines multimedia field measurements and the fugacity model, providing a novel approach to predicting the potential environmental behaviors of PCBs.

Driving Forces of Meteorology and Emission Changes on Surface Ozone in the Huaihe River Basin, China.

Surface ozone (O3) pollution in China has become a serious environmental problem in recent years. In the present study, we targeted the HRB, a large region located in China's north-south border zone, to assess the driving forces of meteorology and emission changes on surface ozone. A Kolmogorov-Zurbenko (KZ) filter method was performed on the maximum daily average 8-h (MDA8) concentrations of ozone in the HRB during 2015-2020 to decompose the original time series. The findings demonstrated that the short-term (O3ST), seasonal (O3SN), and long-term components (O3LT) of MDA8 O3 variations accounted for 34.2%, 56.1%, and 2.9% of the total variance, respectively. O3SN has the greatest influence on the daily variation in MDA8 O3, followed by O3ST. In coastal cities, the influence of O3ST was enhanced. The influence of O3SN was stronger in the northwestern HRB. Air temperature is the prevailing variable that influences the photochemical formation of ozone. A clear phase lag (7-34 days) of the baseline component between MDA8 O3 and the atmospheric temperature was found in the HRB. Using multiple linear regression, the effect of temperature on ozone was removed. We estimated that the increase in ozone concentration in the HRB was mainly caused by the emission changes (79.4%), and the meteorological conditions made a small contribution (20.6%). This study suggests that reductions in volatile organic compounds (VOCs) will play an important role in further ozone pollution reduction in the HRB. Supplementary Information: The online version contains supplementary material available at 10.1007/s11270-023-06345-1.

[Distribution Characteristics and Risk Assessment of PPCPs in Surface Water and Sediments of Lakes in the Lower Reaches of the Huaihe River].

In order to understand the occurrence characteristics and ecological risks of pharmaceuticals and personal care products (PPCPs) in surface water and sediments of Hongze Lake and Gaoyou Lake in the lower reaches of the Huaihe River, 43 surface water and sediment samples from 23 sampling sites were collected, and 61 PPCPs were detected in the samples. The concentration level and spatial distribution of target PPCPs in Hongze Lake and Gaoyou Lake were analyzed, the distribution coefficient of typical PPCPs in the water/sediment system in the study area was calculated, and the ecological risk of target PPCPs was evaluated using the entropy method. The results showed that the PPCPs in surface water of Hongze Lake and Gaoyou Lake were 1.56-2534.44 ng·L-1 and 3.32-1027.47 ng·L-1, respectively, and those in sediment were 1.7-926.7 ng·g-1 and 1.02-289.37 ng·g-1, respectively. The concentrations of lincomycin (LIN) in surface water and doxycycline (DOX) in sediment were the highest, and antibiotics were the main components. The spatial distribution of PPCPs was higher in Hongze Lake and lower in Gaoyou Lake. The distribution characteristics of typical PPCPs in the study area showed that typical PPCPs tended to stay in the water phase, and there was a significant correlation between lg Koc and lg Kd, indicating that total organic carbon (TOC) played an important role in the distribution of typical PPCPs in the water/sediment system. The ecological risk assessment results showed that the ecological risk of PPCPs to algae in surface water and sediment was significantly higher than that of fleas and fish, the ecological risk value of PPCPs in surface water was higher than that in sediment, and the ecological risk of Hongze Lake was higher than that of Gaoyou Lake.

Study on land use and landscape pattern change in the Huaihe River Ecological and economic zone from 2000 to 2020.

Exploring the relationship between land use change and landscape patterns can provide a basis for regional ecological management. In this paper, based on remote sensing images of the Huaihe River Ecological and Economic Zone for the years 2000, 2005, 2010, 2015 and 2020, the spatial and temporal evolution patterns of land use in the region were quantitatively described by using the methods of land use shift matrix and landscape pattern analysis. The relationship between land use change and landscape pattern was analyzed with the Grey Relation Analysis (GRA) model. The results show that: (1) the land use of the Huaihe River Ecological and Economic Zone has changed significantly in the past 20 years, with the conversion of arable land into construction and forest lands, in addition to the growth of water areas and a decline in the areas of arable land, grassland and unused land. (2) The landscape pattern fragmentation of each type of land in the study area from 2000 to 2020 fluctuated and decreased, and the landscape connectivity and landscape diversity increased significantly. (3) The GRA model shows that construction, arable and forest lands played the most significant role in the change of landscape pattern of the Huaihe River Ecological and Economic Zone. Countermeasures are proposed to better coordinate and optimize the relationship between spatial development and landscape pattern for the Huaihe River ecological and economic Zone.

Polycyclic aromatic compounds (PACs) in tree barks and tree cores of a national large-scale coal-fired power base of China: Sources, atmospheric toxicities, and pollution histories.

Polycyclic aromatic compounds (PACs) are important hazardous air pollutants in China due to the country's coal-dominant energy structure. In order to reveal the pollution characteristics, sources, toxicity, and pollution historical trends of PACs in the atmosphere of the middle reach of the Huaihe River (MRHR), a large-scale coal-fired power base of China, tree barks and tree cores were collected and employed as passive air samplers and historical trend recorders, and 76 PACs were identified for the first time. ΣPACs in tree barks ranged from 170 to 3800 ng g-1 (mean = 700 ± 720 ng g-1), with the high concentrations observed mainly in the coal-mining and coal-bearing area. 16 priority PAHs (PriPAHs) were the predominant substances and accounted for 59 ± 8.3 % of ΣPACs. The combustion of coal and fuel oil was the most significant source of PACs, accounting for 43 % of ΣPACs, followed by the combustion of biomass (30 %) and non-combustion sources (27 %). Based on a bark-air partitioning model, volumetric air concentrations for ΣPACs were calculated to be 450-11,000 ng m-3 (mean = 1600 ± 2000 ng m-3). The BaP-toxic equivalent concentrations (TEQBaP) of ΣPACs (mean = 9.7 ± 15 ng m-3) were significantly higher than the Chinese guideline (1 ng m-3) and were mostly caused by coal & fuel oil combustion (55 ± 13 %). High molecular weight PACs were detected in lower percentages in tree cores than in tree barks, indicating that PACs in the particle phase were difficult to enter the tree core. Major PACs decreased in tree core samples between 2000 and 2020 as pollution control efforts improved, however, some PACs showed different trends when influenced by point sources.

Simulation of land use trends and assessment of scale effects on ecosystem service values in the Huaihe River basin, China.

Land use demand change in the Huaihe River basin (HRB) and ecosystem service values (ESVs) in watersheds are important for the sustainable development and use of land resources. This paper takes the HRB as the research object, and using remote sensing images of land use as the data source adopts the comprehensive evaluation analysis method of ESVs based on equivalent factors and sensitivity analysis of the performance characteristics of ESV changes of different land use types. The PLUS model is used to predict spatiotemporal land use change characteristics to 2030 combining inertial development, ecological development, and cultivated land development. The spatial distribution and aggregation of ESVs at each scale were also explored by analyzing ESVs at municipal, county, and grid scales. Considering also hotspots, the contribution of land use conversion to ESVs was quantified. The results showed that (1) from 2000 to 2020, cultivated land decreased sharply to 28,344.6875 km2, while construction land increased sharply to 26,914.563 km2, and the change of other land types was small. (2) The ESVs in the HRB were 222,019 × 1012 CNY in 2000, 235,015 × 1012 CNY in 2005, 234,419 × 1012 CNY in 2010, 229,885 × 1012 CNY in 2015, and 224,759 × 1012 CNY in 2020, with an overall fluctuation, first increasing and then decreasing. (3) The ESVs were 219,977 × 1012 CNY, 218,098 × 1012 CNY, 219,757 × 1012 CNY, and 213,985 × 1012 CNY under the four simulation scenarios of inertial development, ecological development, cultivated land development, and urban development, respectively. At different scales, the high-value areas decreased, and the low-value areas increased. (4) The hot and cold spots of ESV values were relatively clustered, with the former mainly clustered in the southeast region and the latter mainly clustered in the northwest region. The sensitivity of ecological value was lower than 1, while the ESV was inelastic to the ecological coefficient, and the results were plausible. The mutual conversion of cultivated land to water contributed the most to ESVs. Based on the multi-scenario simulation of land use in the HRB by the PLUS model, we identified the spatial distribution characteristics of ESVs at different scales, which can provide a scientific basis and multiple perspectives for the optimization of land use structure and socio-economic development decisions.

Chemical speciation and bioavailability of potentially toxic elements in surface sediment from the Huaihe River, Anhui Province, China.

In order to understand the characteristics of speciation and ecological risk of potentially toxic element (PTE) pollution in the surface sediment of huaihe river (Anhui province), 23 surface sediment samples were collected. The occurrence characteristics of PTEs (As, Cr, Zn, Cu, Cd, Pb, Mn) were analyzed by modified continuous extraction method (BCR), and the pollution status and potential ecological risk of PTEs were comprehensively evaluated by Pollution Load Index (PLI), Geoaccumulation Index (Igeo), Enrichment Factor (EF) and the risk assessment code (RAC). Results showed that the total concentrations of As, Mn, Cd, Cr, Cu, Pb, and Zn in sediment were 14.98 ± 2.32, 936.02 ± 144.48, 0.32 ± 0.08, 161.73 ± 124.83, 40.44 ± 9.67, 15.46 ± 6.67, and 74.85 ± 26.43 mg/kg, respectively. The mean concentrations of PTEs with the increasing order of Zn < Mn < Cr < Pb < Cu < As < Cd. Most PTEs appeared to mainly associate with a dominant proportion of residual fraction suggesting lower mobility whereas Cd and Mn presented a relative higher exchangeable fraction indicating a great degree of bioavailability and easily ingested by aquatic organism. Results of pollution degree showed that 3 sampling sites belong to the pollution degree of strong pollution, and the other sampling sites belonged to the medium pollution level. The indexes EF revealed moderately enrichment of Cr, minor enrichment of Cd, Mn and As, no enrichment of Cu, Zn and Pb. The values of the Igeo and RAC demonstrated that Cd and Mn pose a high ecological risk, which deserves further attention.

Chemical and mineralogical variability of sediment in a Quaternary aquifer from Huaihe River Basin, China: Implications for groundwater arsenic source and its mobilization.

Arsenic (As) is a conspicuous contaminant, and exposure to this element through contaminated drinking groundwater poses a significant challenge to public health. Geogenic groundwater arsenic is associated with sedimentary setting. This work concentrates on the investigation of lithology, elemental abundance and mineralogical compositions about the arsenic profile and its effect to the groundwater from Huaihe River Basin, China. There are 90 sediment samples from the borehole at the field monitoring sites were collected and analyzed. The results reveal that sedimentary concentrations of As, Fe, Mn, S, Al, N, organic carbon and mineralogical compositions vary across the Quaternary aquifer. Arsenic abundance of sediments is 10.63 ± 0.56 mg/kg, and peak As concentrations occur between 59.0 m and 64.8 m in fine particle sediments. The specific higher As concentrations in sedimentary aquifer are concordant with arsenic-rich groundwater around the investigated borehole. Fe, Mn, and Al depth profiles follow similar tendency to those of As. Sedimentary As concentrations are significantly correlated to Fe, Al, and Mn concentrations, but are not correlated to organic carbon and S concentrations. Arsenic probably exists in the form of non-crystalline colloids, and Fe, Al minerals are potential host minerals for arsenic. Under alkaline conditions, groundwater arsenic is released and enriched within the Quaternary aquifer by reductive dissolution of As-hosting Fe and Al minerals.

Using an affinity analysis to identify phytoplankton associations.

Phytoplankton functional traits can represent particular environmental conditions in complex aquatic ecosystems. Categorizing phytoplankton species into functional groups is challenging and time-consuming, and requires high-level expertise in species autecology. In this study, we introduced an affinity analysis to aid the identification of candidate associations of phytoplankton from two data sets comprised of phytoplankton and environmental information. In the Huaihe River Basin with a drainage area of 270,000 km2 in China, samples were collected from 217 selected sites during the low-water period in May 2013; monthly samples were collected during 2006-2011 in a man-made pond, Dishui Lake. Our results indicated that the affinity analysis can be used to define some meaningful functional groups. The identified phytoplankton associations reflect the ecological preferences of phytoplankton in terms of light and nutrient acquisition. Advantages and disadvantages of applying the affinity analysis to identify phytoplankton associations are discussed with perspectives on their utility in ecological assessment.

Measurement of HONO flux using the aerodynamic gradient method over an agricultural field in the Huaihe River Basin, China.

To investigate nitrous acid (HONO) levels and potential HONO sources above crop rotation fields. The HONO fluxes were measured by the aerodynamic gradient (AG) method from 14 December 2019 to 2 January 2020 over an agricultural field in the Huaihe River Basin. The ambient HONO levels were measured at two different heights (0.15 and 1.5 m), showing a typical diurnal cycle with low daytime levels and high nighttime levels. The upward HONO fluxes were mostly observed during the day, whereas deposition dominated at night. The diurnal variation of HONO flux followed solar radiation, with a noontime maximum of 0.2 nmol/(m2∙sec). The average upward HONO flux of 0.06 ± 0.17 nmol/(m2∙sec) indicated that the agricultural field was a net source for atmospheric HONO. The higher HONO/NO2 ratio and NO2-to-HONO conversion rate close to the surface suggested that nocturnal HONO was formed and released near the ground. The unknown HONO source was derived from the daytime HONO budget analysis, with an average strength of 0.31 ppbV/hr at noontime. The surface HONO flux, which was highly correlated with the photolysis frequency J(NO2) (R2 = 0.925) and the product of J(NO2) × NO2 (R2 = 0.840), accounted for ∼23% of unknown daytime HONO source. The significant correlation between HONO fluxes and J(NO2) suggests a light-driven HONO formation mechanism responsible for the surface HONO flux during daytime.

Organochlorine pesticides in riparian soils and sediments of the middle reach of the Huaihe River: A traditional agricultural area in China.

Distributions, souces, ecological risks as well as environmental behaviors of 20 organochlorine pesticides (OCPs) in riparian soils and sediments of the middle reach of the Huaihe River, a traditional agricultural area of China, were investigated. ∑OCPs in riparian soils and sediments were 1.8-63 ng g-1 (mean = 19 ± 12 ng g-1) and 1.2-9.9 ng g-1 (mean = 3.0 ± 1.8 ng g-1), respectively. HCHs were the dominant OCPs in both soils and sediments, while high concentrations of ∑HEPTs and ∑DDTs were also detected in some soils and sediments. No correlations were found between concentrations of OCPs and organic matter contents in both soils and sediments. Based on the source analysis, most OCPs in the riparian soils were mainly from historical residues, such as historical usage of technical HCH, DDT, chlordane and endosulfan. OCPs in sediments were influenced not only by surface runoff by also by other factors, e.g. in-situ contamination (DDT-containing antifouling paints in ships) and/or hydraulic transport from some tributaries. Some never-used OCPs, such as heptachlor and aldrin, were widely detected in soils and sediments. This might be attributed to some unknown usages or long-range atmospheric transport of them from other source regions. Ecological risk analysis suggested that DDTs and HCHs in soils would not lead to an adverse effect on soil ecological environment as well as agricultural production, and OCP residues in sediments also would not pose a threat to the sediment-dwelling organisms.

The quantification of the influencing factors for spatial and temporal variations in surface water quality in recent ten years of the Huaihe River Basin, China.

Temporal and spatial variations in river water quality and the factors influencing such variations are important basis and prerequisites for identifying pollution sources and improving river water environment. Monthly data for 22 indicators at 485 surface water quality (SWQ) monitoring sites (46,560 groups) in the Huaihe River Basin (HRB) from 2011 to 2018 were analyzed. This paper assessed temporal and spatial changes in SWQ in the HRB and identified the main factors influencing the changes and each factor's contribution to the changes. The five-day biological oxygen demand, permanganate index, fluoride, ammonium nitrogen, and total phosphorus were the main pollutants. Spatial cluster analysis indicated that the HRB could be divided by SWQ into areas I-IV from light to heavy pollution. Areas I and IV were nitrogen and phosphorus nutrients pollution, and areas II and III were heavy metals and organic pollution. Area IV (poor SWQ) locates mainly north of the Huaihe mainstream with annual average rainfall ≤ 640 mm. SWQ in the HRB has been improving for two decades, with an inflection point in 2015 between 2011 and 2018, and rainfall change is an important factor for the inflection point. The urbanization rate, industrial water consumption, and rainfall were the key factors influencing SWQ changes in the watershed with significant hydrological zonation, with urbanization rate and rainfall increased, industrial water consumption decreased, the SWQ was gradually improved. The key factors contributing to SWQ changes in the future will be the sewage treatment rate and rainfall changes caused by natural variations.

A novel method for quantifying human disturbances: A case study of Huaihe River Basin, China.

Human disturbances have become the main factors affecting the ecological environment. Therefore, evaluating the intensity of human disturbances is of great significance for ensuring effective regional conservation and ecosystem management. In this study, we constructed a novel method to quantify human disturbances based on three components of human disturbances into three types, namely naturalness transformation, natural resource consumption, and pollutant emissions. These components were quantified using the land use naturalness index (LNI), resource consumption index (RCI), and pollution emission index (PEI). Based on these three indicators, the human disturbances index (HDI) was calculated to reflect the intensity of human disturbances. In addition, remote sensing (RS), geographic information system (GIS), and multisource data were combined in the HDI method, taking into account the temporal variability of input parameters to achieve more convenient and comprehensive dynamic monitoring and evaluation of human disturbances. The applicability and effectiveness of the HDI method were assessed in the Huaihe River Basin, China. The obtained results revealed an increase and decrease in the intensities of human disturbances in the Huaihe River Basin from 1990 to 2005 and from 2010 to 2018, respectively. In addition, areas with a high level of human disturbances in the 1990-2005 period were mainly concentrated in the agricultural and industrial areas, while those in the 2010-2018 period were mainly observed in urban areas. This change was mainly due to a decrease in the pollutant emission amounts from agricultural and industrial lands and a marked increase in resource consumption in urban areas. This study provides theoretical guidance for regional conservation in the Huaihe River Basin and a new method for quantifying human disturbances.

Characteristics of the Phytoplankton Community Structure and Water Quality Evaluation in Autumn in the Huaihe River (China).

As an important indicator of phytoplankton in water quality evaluation, the phytoplankton community structure is very sensitive to changes in water quality, and analyzing their community composition and function is of great significance for the ecological management and maintenance of watershed environments. To understand the environment and ecological status as well as reconstruct or restore a healthy aquatic ecosystem in the Huaihe River Basin in China, a comprehensive phytoplankton survey was conducted in the main stream and main tributaries of the Huaihe River in 2019. A total of 266 species or genera of phytoplankton were identified, mainly belonging to Bacillariophyta and Chlorophyta. The number of phytoplankton species upstream and downstream was higher than that in the middle. The results of phytoplankton biomass showed significant spatial differences in different river reaches (p < 0.05). The identified phytoplankton functional groups (FGs) were divided into 27 groups, including 16 representative functional groups (RFGs), followed by A, B, F, G, H1, J, K, LM, LO, M, MP, P, T, TB, WO and X2. The mean values of the Shannon-Wiener index and Margalef index were 2.47 and 2.50, respectively, showing that most of the water in the Huaihe River Basin was in a state of moderate nutritional status. The results of this study provided a reference for studying the composition and distribution of phytoplankton communities, nutrient status, and pollution levels in the Huaihe River Basin, as well as in other similar watersheds.

Air Pollution and Household Medical Expenses: Evidence From China.

By matching air quality index (AQI) data with the household data from China Family Panel Studies (CFPS), we identify the impact of air pollution on household medical expenses from a micro perspective. The results show that higher air pollution will increase household medical expenses and change household consumption structure. This effect is still significant after controlling for cities' relevant household and individual characteristics and economic characteristics. Under different educational backgrounds, income, hukou, gender, and other conditions, air pollution will significantly reduce medical spending. For those females in the urban areas with higher education backgrounds and higher income, the spending elasticity of air pollution is more significant than other corresponding groups. And air pollution will promote medical expenses through stronger individuals' environmental awareness, poor health conditions, bad emotional status, and positive risk aversion. Furthermore, we find that the impact of air pollution on healthcare spending remains significant after instrumental variables regression and geographical regression based on the Qinling Mountains-Huaihe River Line.