Ecological risk of polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) in the sediment of a protected karst plateau lake (Caohai) wetland in China.

Understanding PAH and OCP distributions and sources in lakes is necessary for developing pollutant control policies. Here, we assessed the occurrence, risk, and sources of PAHs and OCPs in the sediment of Caohai Lake. The PAHs were predominantly high-molecular-weight compounds (mean 57.5 %), and the diagnostic ratios revealed that coal, biomass burning, and traffic were the sources of PAHs. HCHs (6.53 ± 7.22 ng g-1) and DDTs (10.86 ± 12.16 ng g-1) were the dominant OCPs and were primarily sourced from fresh exogenous inputs. RDA showed that sediment properties explained 74.12 % and 65.44 % of the variation in PAH and OCP concentrations, respectively. Incremental lifetime cancer risk (ILCR) assessment indicated that hazardous PAHs in Caohai Lake sediment posed moderate risks to children and adults (ILCR>1.0 × 10-4), while the risk from OCPs was low; however, the recent influx of HCHs and DDTs requires additional attention.

Assessment and sources of heavy metals in the suspended particulate matter, sediments and water of a karst lake in Guizhou Province, China.

An integrated assessment of heavy metal (HM) contamination in dissolved matter, suspended particular matter (SPM) and sediments in lakes is essential. This study assessed the risks of HMs in the water, SPM and sediment of Caohai, China, and analyzed the changes in sediment HM contamination in conjunction with historical data. The HM transport was dominated by the SPM load, and the concentrations of Zn (179.07-1821.24 mg kg-1), Pb (53.63-181.46 mg kg-1), and Cd (3.68-21.31 mg kg-1) in SPM and sediment were 5.34-149.11 times higher than the upper continental crust (UCC) values. SPM and sediments were lightly to extremely polluted and had moderate to very high toxicity risks with Cd, Pb and Zn, and these three HMs originated from industrial and agricultural sources. The concentrations of Zn, Pb, and Cd in Caohai sediments increased by 36.7-187.9 % in 2022 compared to pre-2020. This research provides valuable reference data for the remediation of polluted karst lakes.

Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds.

Classification trees (CT) have been used successfully in the past to classify aquatic vegetation from spectral indices (SI) obtained from remotely-sensed images. However, applying CT models developed for certain image dates to other time periods within the same year or among different years can reduce the classification accuracy. In this study, we developed CT models with modified thresholds using extreme SI values (CT(m)) to improve the stability of the models when applying them to different time periods. A total of 903 ground-truth samples were obtained in September of 2009 and 2010 and classified as emergent, floating-leaf, or submerged vegetation or other cover types. Classification trees were developed for 2009 (Model-09) and 2010 (Model-10) using field samples and a combination of two images from winter and summer. Overall accuracies of these models were 92.8% and 94.9%, respectively, which confirmed the ability of CT analysis to map aquatic vegetation in Taihu Lake. However, Model-10 had only 58.9-71.6% classification accuracy and 31.1-58.3% agreement (i.e., pixels classified the same in the two maps) for aquatic vegetation when it was applied to image pairs from both a different time period in 2010 and a similar time period in 2009. We developed a method to estimate the effects of extrinsic (EF) and intrinsic (IF) factors on model uncertainty using Modis images. Results indicated that 71.1% of the instability in classification between time periods was due to EF, which might include changes in atmospheric conditions, sun-view angle and water quality. The remainder was due to IF, such as phenological and growth status differences between time periods. The modified version of Model-10 (i.e. CT(m)) performed better than traditional CT with different image dates. When applied to 2009 images, the CT(m) version of Model-10 had very similar thresholds and performance as Model-09, with overall accuracies of 92.8% and 90.5% for Model-09 and the CT(m) version of Model-10, respectively. CT(m) decreased the variability related to EF and IF and thereby improved the applicability of the models to different time periods. In both practice and theory, our results suggested that CT(m) was more stable than traditional CT models and could be used to map aquatic vegetation in time periods other than the one for which the model was developed.