A new concern about surface water quality is the occurrence of emerging contaminants that have being recognized as a new class of water contaminants such as antibiotics, hormones, pesticides, personal care products and pharmaceutical products. The occurrence of these contaminants in the aquatic environment and especially in surface water is a serious concern because this is usually the source of water for drinking water treatment plants (DWTP). This review provides a summary of the occurrence and the analytical methodology (extraction process, chromatography analysis, detection systems and ionization source) of emerging contaminant analysis in surface waters including rivers, lakes, creeks and wetlands for their analysis.

A laboratory scale anaerobic/aerobic (An/Ar) system, comprising an anaerobic filter (AF) coupled to an aerobic sequential batch reactor (SBR), was developed to treat wastewater from a slaughterhouse. The AF operated with organic loadings (OL) from 3.7 to 16.5 kg m⁻³ d⁻¹ and a hydraulic retention time (HRT) ranging from 16 to 72 h. The efficiency of chemical oxygen demand (COD) removal was between 50 and 81% and was shown to be related inversely to the value of the OL. The production rate of methane was in the region of 411 mL per g of COD removed. On the other hand, the degradation of organic matter (OM) by an aerobic pathway in the SBR followed first‐order kinetics with regard to OM concentration; 85% of the remaining OM from the AF was eliminated within 6 h of aeration, and over 95% of total OM was eliminated as COD within 9 h. The optimal treatment conditions in this system were found at OL = 11.0 kg m⁻³ d⁻¹ and HRT = 24 h in the AF, whereas the SBR was most efficient at 9 h of aeration.

The combined effects of three key ozonation process parameters on the integrated ozonation-enzymatic hydrolysis pretreatment of sugarcane bagasse (SCB) were investigated, with emphasis on the relationship between sugar release and ozone consumption. A lab-scale fixed bed reactor was employed for ozonation at varying ozone doses (50, 75 and 100 mg O₃/g SCB), particle sizes (420, 710 and 1000 µm) and moisture contents (30, 45 and 60% w/w) in multifactorial experiments, keeping a residence time of 30 min. The ozonated SCB showed a reduction in the content of acid-insoluble lignin from 26.6 down to 19.1% w/w, while those of cellulose and hemicellulose were retained above 45.5 and 13.6% w/w, with recoveries of 100–89.9 and 83.5–72.7%, respectively. Ozone-assisted enzymatic hydrolysis allowed attaining glucose and xylose yields as high as 45.0 and 37.8%, respectively. The sugars released/ozone expended ratio ranged between 2.3 and 5.7 g sugars/g O₃, being the higher value achieved with an applied ozone input of 50 mg O₃/g SCB and SCB with 420 µm particle size and 60% moisture. Such operating conditions led to efficient ozone utilization (<2% unreacted ozone) with a yield of 0.29 g sugars/g SCB. Overall, the amount of sugars released relative to the ozone consumed was improved, entailing an estimated cost of ozonation of USD 34.7/ton of SCB, which could enhance the profitability of the process.

Temporal delays and spatial randomness between ground-based data and satellite overpass involve important deviations between the empirical model output and real data; these are factors poorly considered in the model calibration. The inorganic matter-generated turbidity in Lake Chapala (Mexico) was taken as a study case to expose the influence of such factors. Ground-based data from this study and historical records were used as references. We take advantage of the at-surface reflectance from Landsat-8, sun-glint corrections, a reduced NIR-band range, and null organic matter incidence in these wavelengths to diminish the physical phenomena-related radiometric artifacts; leaving the spatio-temporal relationships as the principal factor inducing the model uncertainty. Non-linear correlations were assessed to calibrate the best empirical model; none of them presented a strong relationship (<73%), including that based on hourly delays. This last model had the best predictability only for the summer-fall season, explaining 71% of the turbidity variation in 2016, and 59% in 2017, with RMSEs < 24%. The instantaneous turbidity maps depicted the hydrodynamic complexity of the lake, highlighting a strong component of spatial randomness associated with the temporal delays. Reasonably, robust empirical models will be developed if several dates and sampling-sites are synchronized with more satellite overpasses.