Spatiotemporal analysis and prediction of water quality in Pearl River, China, using multivariate statistical techniques and data-driven model.

Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81-7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality.

Dissolved metal assessment in surface seawater: A spatial-seasonal evaluation in the Zhejiang coastal waters, the East China Sea.

The spatial-seasonal distributions and variations, correlations with environmental variables and the pollution degrees of dissolved metals in the Zhejiang coastal seawater were investigated. The concentrations of six dissolved metals (i.e. Cu, Pb, Zn, Cd, As and Hg) were in the ranges of 0.10-6.40 (1.6 ± 0.8), 0.16-3.60 (1.2 ± 0.7), 2.50-24.0 (8.5 ± 4.8), 0.011-0.180 (0.07 ± 0.03), 0.85-4.20 (2.1 ± 0.8) and 0.001-0.110 (0.06 ± 0.02) µg/L, throughout the four seasons, respectively. Significant differences in all the dissolved metals were found among seasons, whereas no significant differences were found among stations. The average concentrations of metals were in the following order: Zn > As>Cu > Pb > Cd > Hg. Single metal contamination factor was in the following order: Pb > Hg > Zn > Cu > As>Cd. The pollution level of dissolved metals in the Zhejiang coastal waters (ZCW) was low. Most of the dissolved metals were correlated to temperature, indicating seasonal differences. The redundancy analysis (RDA) indicated that depth, temperature, nitrate and phosphate could best explain the variance pattern of dissolved metals in the ZCW.

Water quality characteristics, sources, and assessment of surface water in an industrial mining city, southwest of China.

This study analyzed the physiochemical factors, spatial-seasonal variations, and correlations of main pollutants, water quality evaluation and possible sources of nitrogen in the surface water of Anning, an industrial mining city, southwest of China. Seventy surface water samples were examined through an analysis of 41 physiochemical indices in the dry and wet seasons in April and July 2019, respectively, while a part of water site samples collected in July 2020 was taken for isotope detections. To identify the water quality, single-factor pollution index (SI), Nemerow pollution index (NPI), and water quality comprehensive pollution index (CPI) were calculated based on 13 pollutants using GB 3838-2002 class III water standard values. Results pointed to typical pollutants of TN, TP, and F with ranges of l.d.-44.8 (2.00 ± 3.69) mg/L, l.d.-250 (2.07 ± 15.35) mg/L, and l.d.-11 (1.48 ± 7.34) mg/L respectively with high spatial variability. The concentrations of heavy metals present in the water samples followed the sequence: Zn > Ni > Cu > As > Pb > Cd > Hg, and most of the samples showed low values relative to the standard permissible limits. In three methods, the water quality evaluation results of SI method were obviously worse than NPI and CPI methods. The NPI and CPI values had ranges of 0.116-887.40 (8.12 ± 74.89) and 0.03-111.54 (1.17 ± 9.40), respectively; consequently, the water quality was considered generally well, with more than 65% of sites classified as "cleanness" or "sub-cleanness." Most of the values of δ15N and δ18O had ranges of 6.62-20.05‰ and - 6.53-4.70‰, which suggested the livestock manure resources were the possible sources of nitrogen that entered the surface water causing more pollution in the wet season. Part of sites with serious water pollution had very high concentrations of P, F, or heavy metals and might be closely correlated with the point source pollution of phosphate chemical industry or iron ore mining and smelting. The results of this study can provide the basic data for efficient water management and human health protection for local government.

Occurrence of azole and strobilurin fungicides in indoor dust from three cities of China.

Widespread use of fungicides has raised the concern of exposure to them among the general population. However, there are extremely limited studies reporting the occurrence of fungicides in indoor dust in China. This study aimed to determine ten agricultural fungicides in indoor dust samples collected in three cities of China from 2016 to 2019, assess spatial and seasonal variations, and estimate the related exposure via dust ingestion. Six out of ten fungicides including difenoconazole, prochloraz, tebuconazole, tricyclazole, azoxystrobin, and pyraclostrobin were frequently detected in the dust samples (ranged 65.8-97.7%) and the concentrations of some fungicides showed a strong correlation with each other. Difenoconazole was the most abundant one among the selected fungicides. The highest level of the selected fungicides was observed in the indoor dust collected from Wuhan in summer 2019 (median cumulative concentration of the fungicides: 62.6 ng/g), while the relatively low concentrations of fungicides were found in the dust from Taiyuan (2.08 ng/g). Heavier fungicide contamination was observed in urban districts compared to that in rural districts. Seasonal variations in the fungicide residuals were also identified. The exposure assessment suggested that intake of the selected fungicides via dust ingestion was much lower than dietary intake reported in other studies. This study filled the data gap of fungicide residuals in the indoor dust in China and further studies are needed to identify the sources and determinants of indoor fungicide contamination.

Distribution, source and risk assessment of heavy metals in the seawater, sediments, and organisms of the Daya Bay, China.

Cu, Pb, Zn, Cd, Cr, Hg and As in seawater, sediment and organisms of the Daya Bay, Guangdong province, China were measured to acquire the comprehensive understanding on distribution, sources and risk assessment of heavy metals (HMs) in the marine ecosystem. The concentrations were relatively ideal, and the Pb was the major pollutant in the seawater and sediment. The contents of HMs were highest in spring; the concentrations near the sewage outlet and shore were noticeable. Submarine pipeline sewage, atmospheric deposition and runoff were the main sources of HMs in coastal waters. Studied HMs were preferentially retained by liquid phase; Cd, Cu and Zn were the most accumulated elements in the organisms from the surrounding environment. Cd in shellfish deserved particular attention, but the health risks including non-carcinogenic and carcinogenic risks of all elements were within acceptable limits. The potential health risks of Pb have been confirmed by molecular docking.

The geochemical behavior of trace metals and nutrients in submerged sediments of the Three Gorges Reservoir and a critical review on risk assessment methods.

Permanently submerged sediment samples (SS) were collected in the center stream of eleven tributaries of Changjiang (Yangtze River) and at eight confluence zones in the Three Gorges Reservoir (TGR) in May and December of 2017. The work showed that aqua regia digestion is a simpler, more reliable and robust method compared to total digestion with hydrofluoric acid (HF) for the determination of trace metals (TMs) in sediment for risk assessment purpose. Our study revealed a remarkable accumulation of TMs at the confluence zones and a trend of their gradual increase toward this zone. The presence of iron and manganese (oxy)hydroxides combined with hydrodynamic conditions created by the Three Gorges Dam (TGD) and its operation are believed to play a crucial role. This work also found that concentrations of [Formula: see text] in May sediment were significantly higher than those in December, which could have been caused by both the cyclic hydrodynamic conditions and the warmer water. TOC and TP were both very low in the sediment. Although TN was 2 times higher than the Lowest Effect Level suggested by the Ontario Ministry of Environment, it is uncertain if it reflects a natural background level or due to anthropogenic activities. A critical discussion is made by comparing the conclusions obtained when using different TMs risk assessment models. Necessary precautions are highly recommended when performing this exercise. In this study, no significant risk from either TMs or nutrients was identified.

A workflow for spatio-seasonal hydro-chemical analysis using multivariate statistical techniques.

Multivariate statistical techniques are powerful in data interpretation and pattern recognition, which play a vital role in pollutant source identification for water environment management. Despite of their wide application in hydro-chemical analysis, absence of a comprehensive workflow hinders the practices and further studies. The present study constructed a workflow on the application of multivariate statistical techniques in spatio-seasonal hydro-chemical analysis, which provided a basic guidance for practices and a systematic support to future exploration. Selection of the methods and work paths for spatio-seasonal analysis largely depends on the structure of data set and the requirements of specific tasks. Trial and adjustment could be repeatedly performed to optimize the analysis strategy and identify more underlying patterns. Given a multiscale dataset concerning complex spatio-seasonal variations, temporal or spatial grouping using appropriate methods to reasonably divide the complicated data set contributes to data interpretation and pattern recognition. The upper Yangtze River basin (UYRB, China) was employed for case analysis to demonstrate how the workflow guides an efficient and effective data exploration. Efforts could be made in future works to continually improve the workflow to involve more complicated analysis and techniques and the integrated application in various fields.

Spatial and seasonal variations of antibiotics and antibiotic resistance genes and ecological risks in the coral reef regions adjacent to two typical islands in South China Sea.

Although the occurrence of antibiotics and antibiotic resistance genes (ARGs) in aquatic environmental has been widely reported, the distribution and variations of these emerging contaminants in the coral reef regions remain unclear. This study investigated the occurrence of these contaminants, and their spatial and seasonal variations in both coral reef regions and non-coral reef regions adjacent to two typical islands in the South China Sea. Eighteen antibiotics and seven ARGs were detected in the surface water with total concentrations ranging from 43.2 to 441 ng/L, and 2.11 × 104 to 8.00 × 106 copies/L, respectively. Erythromycin-H2O was the most dominant antibiotic in all samples. QnrD was dominant in the dry season, whereas sul1, sul2, and floR were the most abundant in the wet season, indicating obvious seasonal variations. The distribution of ARGs was mainly influenced by changes in salinity caused by anthropogenic activities in wet season.

Spatial and seasonal characteristics of dissolved heavy metals in the surface seawater of the Yellow River Estuary, China.

Spatial-seasonal variations, sources and correlations with seawater physiochemical factors and the pollution indexes of dissolved heavy metals in the surface seawater of the Yellow River Estuary in China were investigated. The concentrations of copper (Cu), lead (Pb), zinc (Zn), cadmium (Cd), arsenic (As) and mercury (Hg) had ranges of 0.04-31.0 (11.6 ±â€¯7.08), 0.42-13.3 (5.61 ±â€¯3.55), 1.97-42.2 (14.9 ±â€¯12.0), 0.10-1.90 (0.66 ±â€¯0.37), 0.16-5.89 (2.59 ±â€¯1.12) and 0.10-0.52 µg/L (0.24 ±â€¯0.07), respectively, throughout the four seasons of the year. The concentrations of Cu, Pb, Zn and Cd were significantly high in winter relative to those in the other seasons, and the highest concentrations of Zn and Hg were detected in spring. The single contaminator factors of the elements are in the following order: Pb > Hg > Cu > Zn > Cd > As. The obtained degree of contamination showed that the Yellow River Estuary was highly polluted. The Cu and Zn concentrations were significantly correlated with temperature and suspended particulate matter (SPM). The Cd concentrations were significantly correlated with temperature, SPM, chemical oxygen demand (COD) and dissolved oxygen (DO). The As concentrations were significantly correlated with temperature, salinity, SPM and DO. The Hg concentrations were significantly correlated with salinity. We performed hierarchical cluster and principal component analyses to investigate the possible sources of heavy metals. Agricultural, industrial and atmospheric deposition resources were found to be possible sources of dissolved heavy metals in the studied area. Longshore currents, upwelling and the physicochemical parameters were possible influence factors of dissolved heavy metals spatial and seasonal distribution.

Herbicide micropollutants in surface, ground and drinking waters within and near the area of Zagreb, Croatia.

The frequency and mass concentrations of 13 herbicide micropollutants (triazines, phenylureas, chloroacetanilides and trifluralin) were investigated during 2014 in surface, ground and drinking waters in the area of the city of Zagreb and its suburbs. Herbicide compounds were accumulated from water by solid-phase extraction using either octadecylsilica or styrene-divinylbenzene sorbent cartridges and analysed either by high-performance liquid chromatography with UV-diode array detector or gas chromatography with mass spectrometric detection. Atrazine was the most frequently detected herbicide in drinking (84 % of samples) and ground (61 % of samples) waters in mass concentrations of 5 to 68 ng L-1. It was followed by metolachlor and terbuthylazine, the former being detected in 54 % of drinking (up to 15 ng L-1) and 23 % of ground (up to 100 ng L-1) waters, and the latter in 45 % of drinking (up to 20 ng L-1) and 26 % of ground (up to 25 ng L-1) water samples. Acetochlor was the fourth most abundant herbicide in drinking waters, detected in 32 % of samples. Its mass concentrations of 107 to 117 ng L-1 in three tap water samples were the highest of all herbicides measured in the drinking waters. The most frequently (62 % of samples) and highly (up to 887 ng L-1) detected herbicide in surface waters was metolachlor, followed by terbuthylazine detected in 49 % of samples in mass concentrations of up to 690 ng L-1, and atrazine detected in 30 % of samples in mass concentrations of up to 18 ng L-1. The seasonal variations in herbicide concentrations in surface waters were observed for terbuthylazine, metolachlor, acetochlor, chlortoluron and isoproturon with the highest concentrations measured from April to August.