Rapid monitoring of heavy metal pollution in lake water using nitrogen and phosphorus nutrients and physicochemical indicators by support vector machine.

A novel method of predicting heavy metal concentration in lake water by support vector machine (SVM) model was developed, combined with low-cost, easy to obtain nutrients and physicochemical indicators as input variables. 115 surface water samples were collected from 23 sites in Chaohu Lake, China, during different hydrological periods. The particulate concentrations of heavy metals in water were much higher than the dissolved concentrations. According to Nemerow pollution index (Pi), pollution degrees by Fe, V, Mn and As ranged from heavy (2 ≤ Pi < 4) to serious (Pi ≥ 4). The concentrations of most heavy metals were the highest during the medium-water period and the lowest during the dry season. Non-metric Multidimensional Scaling Analysis confirmed heavy metal concentrations had slight spatial difference but relatively large seasonal variation. Redundancy Analysis indicated the close associations of heavy metals with nutrient and physicochemical indicators. When both nutrient and physicochemical indicators were used as input variables, the simulation effects for most elements in total and particulate were relatively better than those obtained using only nutrient or only physicochemical indicators. The simulation effects for As, Ba, Fe, Ti, V and Zn were generally good, based on their training R values of 0.847, 0.828, 0.856, 0.867, 0.817 and 0.893, respectively, as well as their test R values of 0.811, 0.836, 0.843, 0.873, 0.829 and 0.826, respectively; and meanwhile, in both the training and test stages, these metals also had relatively lower errors. The spatial distribution of heavy metals in Chaohu Lake was then predicted using the fully trained SVM models.

Simulating heavy metal concentrations in an aquatic environment using artificial intelligence models and physicochemical indexes.

Although heavy metal monitoring campaigns are established worldwide, it is still difficult to model heavy metals in aquatic environments with limited monitoring data. In this study, surface water physicochemical indexes and heavy metal concentrations were measured in a drinking water source in the Taihu Lake region, China. Afterwards, indexes including water temperature, pH, suspended matter, turbidity, and total nitrogen, nitrate nitrogen, ammonia nitrogen, total phosphorous, orthophosphate and permanganate index were used to simulate dissolved, particulate and total heavy metal concentrations using artificial neural network (ANN) and support vector machine (SVM) models. Sensitivity analysis showed that simulated heavy metal concentrations were most sensitive to pH. Thereafter, quick simulation models based on five sensitive parameters (pH, suspended matter, water temperature, total phosphorus and permanganate index) allowed for quick simulations of heavy metal concentrations were built. Both ANN and SVM quick simulation models simulated particulate heavy metal concentrations well with most Nash-Sutcliffe efficiency coefficients >0.8. Models performed worse when simulating dissolved and total heavy metal concentrations. Results demonstrate that artificial intelligence models like ANN and SVM are alternative ways to simulate heavy metal concentrations with limited monitoring data. Furthermore, sensitivity analysis may help to identify key factors affecting heavy metal behavior, and to improve environmental monitoring campaigns and management strategies.

Particulate Matter Exposure History Affects Antioxidant Defense Response of Mouse Lung to Haze Episodes.

Few studies have focused on the association between previous particulate matter (PM) exposure and antioxidant defense response to a haze challenge. In this study, a combined exposure model was used to investigate whether and how PM exposure history affected the antioxidant defense response to haze episodes. At first, C57BL/6 male mice were randomly assigned to three groups and exposed for 5 weeks to whole ambient air, ambient air containing a low (≤75 µg/m3) PM concentration, and filtered air, which simulated different exposure history of high, relatively low, and almost zero PM concentrations. Thereafter, all mice underwent a 3-day haze exposure followed by a 7-day exposure to filtered air. The indexes involved in the primary and secondary antioxidant defense response were determined after pre-exposure and haze exposure, as well as 1 day, 3 days, and 7 days after haze exposure. Our research demonstrated repeated exposure to a high PM concentration compromised the antioxidant defense response and was accompanied by an increased susceptibility to a haze challenge. Conversely, mice with a lower PM exposure developed an oxidative stress adaption that protected them against haze challenge more efficiently and in a more timely manner than was the case in mice without PM exposure history.

Adsorption of sulfonamides to demineralized pine wood biochars prepared under different thermochemical conditions.

The main objective of this study was to understand the key factors and mechanisms controlling adsorption of sulfonamides to biochars. Batch adsorption experiments were performed for sulfamethoxazole and sulfapyridine to three pine-wood biochars prepared under different thermochemical conditions: pyrolysis at 400 °C (C400) and 500 °C (C500), and pyrolysis at 500 °C followed with hydrogenation (C500-H). For both sulfonamides, the adsorbent surface area-normalized adsorption was stronger to C500 than to C400. This is attributable to the enhanced π-π electron-donor-acceptor interaction with the carbon surface of C500 due to the higher degree of graphitization. Despite the relatively large difference in surface O-functionality content between C500 (12.2%) and C500-H (6.6%), the two biochars exhibited nearly identical adsorbent surface area-normalized adsorption, indicating negligible role of surface O-functionalities in the adsorption to these adsorbents. Effects of solution chemistry conditions (pH, Cu(2+), and dissolved soil humic acid) on adsorption were examined.