The present study has been conducted to determine the surface water quality of urban area in Savar, Dhaka, Bangladesh by determining some water quality parameters (Transparency, Temperature, pH, EC, Eh, DO, TSS, TDS, TS, BOD5, COD, TOC, Cl-, Br-, SO4-2, NO3-, NO2-, PO4-3, TP, HCO3- and Total alkalinity) as well as the status of phytoplankton’s community in the water from two lakes (Tiger Lake and AERE Lake) and one canal (Karnapara Canal). It has been shown that, with exception of BOD and COD, all water quality parameters of AERE Lake in the present study are within the acceptable limits, recommended by local and international standards. Among water quality parameters of Tiger Lake and Karnapara Canal, the concentration of transparency, temperature, EC, DO, TSS, TDS, BOD, COD, TOC, NO2-, and TP exceed the acceptable limits. Organic Pollution Index (OPI) demonstrate that the water bodies are severely polluted by organic matters. R mode Cluster Analysis (CA) suggests that common sources of water quality parameters are industrial, agricultural, and natural. The Principle Component Analysis/Factor Analysis (PCA/FA) identifies two dominant factors, responsible for data structure, explaining 100% of total variance in the data set. The PCA agrees with CA, suggesting that multiple anthropogenic and natural sources are responsible for the water quality parameters. The present study reflects the actual scenario of surface water quality of Savar urban area, thus helping the policy planers and makers of the People’s Republic of Bangladesh to take proper management and abatement strategies for the management of sustainable water resource in Bangladesh.

The properties and metal-binding abilities of dissolved organic matter (DOM) rely on its molecular weight (MW) structure. In this study, the spatial differences of DOM in compositions, MW structures, and binding mechanisms with copper (Cu²⁺) in Dongping Lake were investigated by applying excitation-emission matrix combining parallel factor analysis (EEM-PARAFAC), synchronous fluorescence (SF) spectra, two-dimensional correlation spectra (2D-COS), and Fourier transform infrared (FTIR) spectra. The EDOM for the entrance of the Dawen River and PDOM for the macrophyte-dominated region were divided from DOM of Dongping Lake based on hierarchical clustering analysis (HCA) and principal component analysis (PCA) and were size-fractioned into MW < 500 kDa and <100 kDa fractions. According to EEM-PARAFAC, Dongping Lake was dominated by tryptophan-like substances with MW < 500 kDa. The concentration of PDOM was higher than that of EDOM (p < 0.05). 2D-COS showed that protein-like components preceded humic-like components binding to Cu²⁺ regardless of sample type (215 nm > 285 nm > 310–360 nm). The Cu²⁺ binding capacity of DOM exhibited specific differences in space, components, and molecular weights. The humic-like component 1 (C1) and tryptophan-like component 4 (C4) of PDOM showed stronger binding abilities than those of EDOM. Endogenous tryptophan-like component 4 (C4) had a higher binding affinity for Cu²⁺ than humic-like components (logKₐ: C4 > C1 > C2) in PDOM irrespective of MW. Humic-like components with MW < 500 kDa displayed higher binding potentials for Cu²⁺. FTIR spectra showed that the main participants of DOM-Cu complexation included aromatic hydrocarbons, aliphatic groups, amide Ⅰ bands, and carboxyl functional groups. This study provides spatial-scale insights into the molecular weight structure of DOM in influencing the behavior, fate, and bioavailability of heavy metals in lakes.

The Yellow River Delta (YRD) wetland, the most important estuary wetland in eastern China, has an important ecosystem service function. Rapid and intensive development has inevitably led to the accumulation of potentially toxic elements (PTEs) in soils. Therefore, identifying quantitative sources and spatial distributions of PTEs is essential for soil environmental protection in the YRD. A total of 240 topsoil samples (0–20 cm) were collected in the Yellow River Delta Nature Reserve (YRDNR) and analyzed the PTE contents. To avoid the biases of the single receptor model, positive matrix factorization, factor analysis with nonnegative constraints, and maximum likelihood principal component analysis-multivariate curve resolution-alternating least squares were used for source apportionment of soil PTEs. To promote the efficiency of multivariate geostatistical simulation, a minimum/maximum autocorrelation factor-sequential Gaussian simulation was built to map the spatial patterns of PTEs. Three factors were derived by the three receptor models, and their contributions to the source explanation were similar. As, Cr, Cu, Mn, Ni, and Zn originated from natural sources, with contributions of 85.6%–96.4 %. A total of 61.5 % of Hg was associated with atmospheric deposition of coal combustion and wastewater from upstream. Agricultural activities and oil exploitation contributed 33.5 % and 15.9 % of the Cd and Pb concentrations. Spatial distributions of soil PTEs were controlled by sedimentary grain size. A total of 47.2 % of the total study area was identified as hazardous area for Cd, 10.3 % for As, and 5.4 % for Hg. This work is expected to provide references for soil pollution assessment and management of YRDNR.

Northern China is a significant source of dust source in Central Asia. Thus, high-resolution analysis of dust storms and comparison of dust sources in different regions of northern China are important to clarify the formation mechanism of East Asian dust storms and predict or even prevent such storms. Here, we analyzed spatiotemporal trends in dust storms that occurred in three main dust source regions during 1960–2007: Taklimakan Desert (western region [WR]), Badain Jaran and Tengger Deserts (middle region [MR]), and Otindag Sandy Land (eastern region [ER]). We analyzed daily dust storm frequency (DSF) at the 10-day scale (first [FTDM], middle [MTDM], and last [LTDM] 10 days of a month), and investigated the association of dust storm occurrences with meteorological factors. The 10-day DSF was greatest in the FTDM (accounting for 77.14% of monthly occurrences) in the WR, MTDM (45.85%) in the MR, and LTDM (72.12%) in the ER, showing a clear trend of movement from the WR to the ER. Temporal analysis of DSF revealed trend changes over time at annual and 10-day scales, with mutation points at 1985 and 2000. We applied single-factor and multiple-factor analyses to explore the driving mechanisms of DSF at the 10-day scale. Among single factors, a low wind-speed threshold, high solar radiation, and high evaporation were correlated with a high DSF, effectively explaining the variations in DSF at the 10-day scale; however, temperature, relative humidity, and precipitation poorly explained variations in DSF. Similarly, multiple-factor analysis using a classification and regression tree revealed that maximum wind speed was a major influencing factor of dust storm occurrence at the 10-day scale, followed by relative humidity, evaporation, and solar radiation; temperature and precipitation had weak influences. These findings help clarify the mechanisms of dust storm occurrence in East Asia.

The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating “good” to “excellent” water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl⁻, SO₄²⁻, COD (chemical oxygen demand), TSS (total suspended solids) and Ca²⁺ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH₄–N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).

Uncertainty assessment of parameters associated with non-point source pollution mechanism modeling are crucial for improving the effectiveness of pollution controlling. In this study, an approach based on Bayesian inference and integrated Markov chain Monte Carlo and multilevel factorial analysis has been developed, and it can not only apply straightforward Bayesian inference to assess parameter uncertainties, but also quantitatively investigate the main and interactive effects of multiple parameters on the model response variables by measuring the specific variations of model outputs. Its applicability and advantages are presented through the application of the Soil and Water Assessment Tool to Shitoukoumen Reservoir Catchment in northeast China. This study investigated the uncertainties of a set of sensitive parameters and their multilevel effects on model response variables, including average annual runoff (AAR), average annual sediment (AAS) and average annual total nitrogen (AAN). Results revealed that (i) soil conservation service runoff curve number for moisture condition II (CN2) had a positive effect on all response variables; (ii) available water capacity of the soil layer (SOL_AWC) had a negative effect on all response variables; (iii) the universal soil loss equation support practice (USLE_P) had a positive effect on AAS and AAN, and little effect on AAR; while the nitrate percolation coefficient (NPERCO) had a positive effect on AAN, and little effect on AAS and AAR; and (iv) the interactions amongst parameters had obvious interdependent effects on the model response variables, for example, the interaction between CN2 and SOL_AWC had a major impact on AAR. The above findings can improve the simulating and predicting capabilities of non-point source pollution mechanism model. Overall, this study highlights that the proposed approach represents a promising solution for uncertainty assessment of model parameters in non-point source pollution mechanism modeling.

The inorganic content of the well-preserved 3.2-m record of Las Conchas bog (NW Spain), covering 8000 cal yr BP., was analysed. To study natural vs. human contributions, we applied an innovative approach, namely the sequential study of multivariate statistics (factor analysis followed by clustering of the factor score matrix) and enrichment factors (EFs). The increasing weight of potentially toxic elements (PTEs) such as the geochemical association of Zn, Pb and Cd (EFs higher than 10, 20 and 40 in the last two centuries) was revealed, and corroborated by the contrast between the contents of anthropogenic Pb and total Rare Earth Elements (a suitable proxy for natural geogenic supplies). Furthermore, elements such as Hg, Tl and As also showed enrichment in the most recent samples of the study core. Some of them are commonly associated with global atmospheric transport; however, in this case, their increasing contents could also be explained by nearby industrial and mining activities.In summary, severe pollution was observed in the uppermost part of the record, thereby pointing to an important environmental concern. Given that local and regional sources of PTEs, such as mining and heavy industry, especially Zn smelting, were probably the main historical causes of this contamination and that some of these industries are still active, we consider that our findings deserve further attention.

Global water safety is facing great challenges due to increased population and demand. There is an urgent need to develop suitable water treatment strategy for small rural and remote communities in low-income developing countries. In order to find a low-cost solution, the reduction of E. coli using ceramic water disk coated with nano ZnO was investigated in this study. The performance of modified ceramic disk filters was influenced by several factors in the filter production process. Based on the factorial analysis, the pore size of the disk filters was the most significant factor for influencing E. coli removal efficiency and the clay content was the most significant one for influencing flow rate of modified disk filters. The coating of nano ZnO led to the change of disk filter surface and porosity. The reduction of E. coli could be attributed to both filter retention and photocatalytic antibacterial activity of nano ZnO. The effects of filter operation factors including initial E. coli concentration, illumination time and lamp power on E. coli removal effectiveness were also revealed. The results can help find a safe and cost-effective approach to solve drinking water problems in small rural and remote communities of developing regions.

A series of field samples including ambient air (gaseous and particulate phases), dust fall, surface soil, rhizosphere soil and cabbage tissues (leaf, root and core), were collected in vegetable bases near a large coking manufacturer in Shanxi Province, Northern China, during a harvest season. A factor analysis was employed to apportion the emission sources of polycyclic aromatic hydrocarbons (PAHs), and the statistical results indicated coal combustion was the dominant emission source that accounted for different environmental media and cabbage tissues, while road traffic, biomass burning and the coking industry contributed to a lesser extent. A structural equation model was first developed to quantitatively explore the transport pathways of PAHs from surrounding media to cabbage tissues. The modeling results showed that PAHs in ambient air were positively associated with those in dust fall, and a close relationship was also true for PAHs in dust fall and in surface soil due to air-soil exchange process. Furthermore, PAHs in surface soil were correlated with those in rhizosphere soil and in the cabbage leaf with the path coefficients of 0.83 and 0.39, respectively. PAHs in the cabbage leaf may dominantly contribute to the accumulation of PAHs in the edible part of cabbages.

Effects of photochemical and microbial degradation on variations in composition and molecular-size of dissolved organic matter (DOM) from different sources (algal and soil) and the subsequent influence on Cu(II) binding were investigated using UV–Vis, fluorescence excitation-emission matrices coupled with parallel factor analysis, flow field-flow fractionation (FlFFF), and metal titration. The degradation processes resulted in an initial rapid decline in the bulk dissolved organic carbon and chromophoric and fluorescent DOM components, followed by a small or little decrease. Specifically, photochemical reaction decreased the aromaticity, humification and apparent molecular weights of all DOM samples, whereas a reverse trend was observed during microbial degradation. The FlFFF fractograms revealed that coagulation of both protein- and humic-like DOM induced an increase in molecular weights for algal-DOM, while the molecular weight enhancement for allochthonous soil samples was mainly attributed to the self-assembly of humic-like components. The Cu(II) binding capacity of algal-derived humic-like and fulvic-like DOM consistently increased during photo- and bio-degradation, while the soil-derived DOM exhibited a slight decline in Cu(II) binding capacity during photo-degradation but a substantial increase during microbial degradation, indicating source- and degradation-dependent metal binding heterogeneities. Pearson correlation analysis demonstrated that the Cu(II) binding potential was mostly related with aromaticity and molecular size for allochthonous soil-derived DOM, but was regulated by both DOM properties and specific degradation processes for autochthonous algal-derived DOM. This study highlighted the coupling role of inherent DOM properties and external environmental processes in regulating metal binding, and provided new insights into metal-DOM interactions and the behavior and fate of DOM-bound metals in aquatic environments.