The increasing availability of water quality datasets has led to a greater focus on hydrologic and water quality analysis, thus requiring more efficient and accurate modelling methods. Data mining techniques have been increasingly used for water quality analysis and prediction of the concentration and load of nitrogen pollutants instead of more traditional simulation methods. In this study, we tested the multilayer perceptron (MLP), k-nearest neighbor (k-NN), random forest, and reduced error pruning tree (REPTree) methods, along with the traditional linear regression, to predict nitrate levels based on long-term data from six watersheds with different land-use practices in the midwestern United States. Both the concentration and load results indicated that REPTree had the best performance, with an R² of 0.61–0.85 and a relative absolute error of <75.8%. The different watershed types, however, influenced the performance of the data mining methods, where all four methods showed a higher accuracy for urban dominant watershed and lower accuracy for agricultural and forest watersheds. Out of these four methods, classification tree methods (REPTree and RF) performed better than cluster methods (MLP and k-NN) for agricultural and forested watersheds. Our results indicated that both the data structure based on the dominant land use and type of algorithmic method should be carefully considered for selecting a data mining method to predict nitrate concentration and load for a watershed.

The air quality in classrooms is a critical factor that affects students’ daily exposure. Although air filtration units (AFUs) are often used to reduce exposure to air pollution in China, the effectiveness of installing AFUs in Chinese classrooms have not been quantitatively studied. Hence, we carried out concurrent air quality experiments in two identical classrooms where one room was equipped with commercial AFUs and the other one relied on the natural introduction of outside air. Measurements were taken during regular school days in the winter at a primary school in Hangzhou, China. Three AFU ventilation modes, i.e., fresh air, mixed air and recirculation mode were evaluated, respectively. We found that classrooms equipped with AFU showed significant PM₂.₅ reductions, but this was accompanied by CO₂ build-ups. In classroom with AFUs on internal recirculation, nearly 70% of the PM₂.₅ concentration at the beginning of the class was reduced by the end of a 40-min class. However, the CO₂ concentration was observed to reach levels that were six times greater than that of the ambient due to inadequate air change rate (<1 h⁻¹). To mitigate the issue of excessive in-classroom CO₂ accumulation when using AFUs, filtered outside air must be brought in during their operation. In comparison with the internal recirculation mode, we demonstrated that the fresh air mode that draws the supply air from outdoor can considerably lower CO₂ build-ups in the classroom with a moderate increase in PM₂.₅ concentration of 15 ± 10 μg/m³. The findings from this study helps policy makers especially in developing countries with serious air pollution issues to determine whether or not to install AFU in primary schools and what ventilation mode is effective in reducing negative health effects.

Characterizing Black Carbon (BC) at regional background areas is important for better understanding its impact on climate forcing and health effects. The variability and sources of Equivalent Black Carbon (EBC) in PM₁₀ (atmospheric particles with aerodynamic diameter smaller than 10 μm) have been investigated during a 5-year measurement period at the National Atmospheric Observatory Košetice (NAOK), Czech Republic. Ground based measurements were performed from September 2012 to December 2017 with a 7-wavelength aethalometer (AE31, Magee Scientific). The contributions of fossil fuel (EBCff) and biomass burning (EBCbb) were estimated using the aethalometer model. Seasonal, diurnal and weekly variations of EBC were observed that can be related to the sources fluctuations and transport characteristic of pollutants predominantly associated with regional air masses recirculating over the Czech Republic and neighboring countries. The absorption Ångström exponent (α-value) estimated in summer (1.1 ± 0.2) was consistent with reported value for traffic, while the mean highest value (1.5 ± 0.2) was observed in winter due to increased EBCbb accounting for about 50% of the total EBC. This result is in agreement with the strong correlation between EBCbb and biomass burning tracers (levoglucosan and mannosan) in winter. During this season, the concentrations of EBCbb and Delta-C (proxy for biomass burning) reached a maximum in the evening when increasing emissions of wood burning in domestic heating devices (woodstoves/heating system) is expected, especially during the weekend. The diurnal profile of EBCff displays a typical morning peak during the morning traffic rush hour and shows a decreasing concentration during weekends due to lower the traffic emission.

In this study, size-fractionated particulate matters (PM) down to ultrafine (PM₀.₁) particles were collected using a cascade air sampler with a PM₀.₁ stage, in Hat Yai city, Songkhla province, southern Thailand during the year 2018. The particle-bound carbonaceous aerosols (CA) as elemental carbon (EC) and organic carbon (OC) were quantified with the thermal/optical reflectance method following the IMPROVE_TOR protocol. The concentrations of different temperature carbon fractions (OC1-OC4, EC1-EC3 and PyO) in the size-fractionated PM were evaluated to discern OC and EC correlations as well as those between char-EC and soot-EC. The results showed that biomass burning, motor vehicle, and secondary organic aerosols (SOC) all contributed to the size-fractionated PM. The OC/EC ratios ranged from 2.90 to 4.30 over the year, with the ratios of PM₂.₅₋₁₀ being the highest, except during the open biomass burning period. The concentration of CA was found to increase during the pre-monsoon season and had its peak value in the PM₀.₅₋₁.₀ fraction. The long-range transport of PMs from Indonesia, southwest of Thailand toward southern Thailand became more obvious during the pre-monsoon season. Transported plumes from biomass burning in Indonesia may increase the concentration of OC and EC both in the fine (PM₀.₅₋₁.₀ and PM₁.₀₋₂.₅) and coarse (PM₂.₅₋₁₀ and PM>₁₀) fractions. The OC fraction in PM₀.₁ was also shown to be significantly affected by the transported plumes during the pre-monsoon season. Good OC and EC correlations (R² = 0.824–0.915) in the fine particle fractions indicated that they had common sources such as fossil fuel combustion. However, the lower and moderate correlations (R² = 0.093–0.678) among the coarser particles suggesting that they have a more complex pattern of emission sources during the dry and monsoon seasons. This indicates the importance of focusing emission control strategies on different PM particle sizes in southern Thailand.

Potassium (K) fertilizer plays an important role in increasing crop yield, quality, and nitrogen use efficiency. However, little is known about its environmental impacts, such as its effects on emissions of the greenhouse gas nitrous oxide (N₂O). A nitrogen-15 (¹⁵N) tracer laboratory experiment was therefore performed in an acidic agricultural soil in the suburbs of Wuhan, central China, to determine the effects of K fertilizer on N₂O emissions and nitrification/denitrification product ratios under N fertilization. During 15-d incubation periods with a fixed initial N concentration (80 mg kg⁻¹), K application increased average N₂O emission rates significantly (1.6–10.8-fold) compared to the control treatment. N₂O emissions derived from nitrification and denitrification both increased in K-treated soil, and denitrification contributed more to the increase; its contribution ratio rose from 32% without K fertilizer to 53% with 300 mg kg⁻¹ of K applied. The increase in N₂O emissions under K fertilization is probably due to an increase in the activity of denitrifying microorganisms and acid-resistant nitrifying microorganisms caused by higher K⁺ concentrations and lower soil pH. Combined treatment with potassium chloride (KCl) and N fertilizer produced lower N₂O emissions than combined treatment with potassium sulfate (K₂SO₄) and N fertilizer during 15-d incubation periods. Our results imply that there are significant interaction effects between N fertilizers and K fertilizers on N₂O emissions. In particular, combining N fertilizers with fertilizers that reduce soil acidity or contain Cl or K ions may significantly affect agricultural N₂O emissions.

Pore water is a crucial storage medium and a key source of sediment phosphorus. A novel equipment based on electrokinetic geosynthetics (EKGs) was used for isolating phosphorus from eutrophic lake sediments through pore water drainage. Three mutually independent indoor group experiments (A, B, and C) were conducted to investigate the effects of voltage gradient (0.00, 0.25, and 0.50 V/cm) on pore water drainage capacity, phosphorus removal performance, sediment physicochemical properties, and phosphorus storage dynamics. The average reduction in the sediment moisture and total phosphorus content was 2.5%, 4.3%, and 4.6% and 28.15, 75.95, and 112.65 mg/kg after 6 days of treatment for A, B and C, respectively. Efficient pore water drainage through gravity and electroosmotic flow and electromigration of phosphate were the main drivers of sediment-dissolved and mobilized phosphorus separation. A high voltage gradient facilitated the migration of pore water and the phosphorus in it. The maximal effluent total phosphorous (TP) concentration was up to 27.9 times that in the initial pore water samples, and negligible effluent TP was detected when the pore water pH was less than 2.5. The TP concentration was exponentially and linearly related to the pH and electronic conductivity of the electroosmotic flow, respectively. The migration of H⁺ within the sediment matrix promoted the liberation of metals bounded to phosphorus, particularly of Ca–P and Fe–P. Pore water drainage through an EKG resulted in Ex–P separation of up to 87.50% and a 13.84 mg/kg decrease in Ca–P and 125.35 mg/kg accumulation of low mobile Fe–P in the weak acid anode zone.

Poultry manure is a reservoir for antibiotics and antibiotic resistance genes and composting is an effective biological treatment for manure. This study explored the effect of using two methods of adding a complex microbial agent to the composting of laying-hen manure on doxycycline degradation and tetracycline resistance genes elimination. The results showed that incorporating a complex microbial agent at 0.8% (w/w) on the 0ᵗʰ and 11th day (group MT2) effectively degraded doxycycline with a final degradation rate of 46.83 ± 0.55%. The half-life of doxycycline in this group was 21.90 ± 0.00 days and was significantly lower than that of group MT1 (1.6% (w/w) complex microbial agent added on the 0ᵗʰ day) and group DT (compost without complex microbial agent). But there was no significant difference in the final degradation rate of doxycycline between group DT and group MT1. The addictive with the complex microbial agent changed the microbial community structure. Bacteroidetes, Firmicutes and Proteobacteria were the dominant phyla during composting. Aerococcus, Desemzia, Facklamia, Lactobacillus, Streptococcus, and Trichococcus were the bacteria related to the degradation of doxycycline. Moreover, the incorporation of a complex microbial agent could decrease the risk on spreading tetracycline resistance genes. The single addition promoted the elimination of tetM, whose possible hosts were Enterococcus, Lactobacillus, Staphylococcus, and Trichococcus. Adding the complex microbial agent twice promoted the elimination of tetX, which was related to the low abundance of Chryseobacterium, Flavobacterium and Neptunomonas in group MT2. Redundancy analysis showed that the bacterial community, residual doxycycline and physiochemical properties have a potential effect on the variation in tetracycline resistance genes levels. Overall, adding the complex microbial agent twice is an effective measure to degrade doxycycline.

Wastewater treatment plants (WWTPs) provide optimal conditions for the maintenance and spread of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). In this work we describe the occurrence of antibiotic resistant faecal coliforms and their mechanisms of antibiotic resistance in the effluent of two urban WWTPs in Ireland. This information is critical to identifying the role of WWTPs in the dissemination of ARB and ARGs into the environment. Effluent samples were collected from two WWTPs in Spring and Autumn of 2015 and 2016. The bacterial susceptibility patterns to 13 antibiotics were determined. The phenotypic tests were carried out to identify AmpC or extended-spectrum β-lactamase (ESBL) producers. The presence of ESBL genes were detected by PCR. Plasmids carrying ESBL genes were transformed into Escherichia coli DH5α recipient and underwent plasmid replicon typing to identify incompatibility groups. More than 90% of isolated faecal coliforms were resistant to amoxicillin and ampicillin, followed by tetracycline (up to 39.82%), ciprofloxacin (up to 31.42%) and trimethoprim (up to 37.61%). Faecal coliforms resistant to colistin (up to 31.62%) and imipenem (up to 15.93%) were detected in all effluent samples. Up to 53.98% of isolated faecal coliforms expressed a multi-drug resistance (MRD) phenotype. AmpC production was confirmed in 5.22% of isolates. The ESBL genes were confirmed for 11.84% of isolates (9.2% of isolates carried blaTEM, 1.4% blaSHV₋₁₂, 0.2% blaCTX₋M₋₁ and 1% blaCTX₋M₋₁₅). Plasmids extracted from 52 ESBL isolates were successfully transformed into recipient E. coli. The detected plasmid incompatibility groups included the IncF group, IncI1, IncHI1/2 and IncA/C. These results provide evidence that treated wastewater is polluted with ARB and MDR faecal coliforms and are sources of ESBL-producing, carbapenem and colistin resistant Enterobacteriaceae.

This study developed hybrid Bayesian models to investigate the modeling process for children’s exposure to soil contaminants, which involves the intrinsic uncertainty of the exposure model, people’s judgments regarding random variables, and limited data resources. A hybrid Bayesian p-box was constructed, which was facilitated by a multiple integral dimensionality reduction (MIDR) theorem. The results indicated that exposure frequency (EF) dominated the exposure dose. The hybrid Bayesian p-box for the Frequentist-Bayesian (F–B) model at the 95th percentile of the simulated average daily dose (ADD) values corresponded to a 4.40 order-of-magnitude difference between the upper and lower bounds of the p-box. This considerable uncertainty was magnified by the combination of the highest posterior density (HPD) regions for three groups of the distribution parameters. For the Interior-Bayesian (I–B) hybrid model, the uncertainty of the outcomes, namely, [1.75 × 10⁻⁸, 2.18 × 10⁻⁸] mg kg⁻¹d⁻¹, was limited by the HPD regions for only one parameter unless the hyperparameters for the variables’ distributions were further evaluated. It was concluded that the hybrid models could provide a novel understanding of the complexity of the exposure modeling process compared to the traditional modeling method.

Epidemiological studies have investigated the associations of bisphenol A (BPA) exposure with hypertension risk or blood pressure levels, but findings are inconsistent. Furthermore, the association between its alternatives bisphenol S and F (BPS and BPF) and hypertension risk are not yet known. We conducted a cross-sectional study in 1437 eligible participants without hypertension-related diseases, with complete data about blood pressure levels, hypertension diagnosis, and urinary bisphenols concentrations. Multivariable logistic and linear models were respectively applied to examine the associations of urinary bisphenols concentrations with hypertension risk and blood pressure levels. The dose-response relationship was explored by the restricted cubic spline model. Compared with the reference group of BPA, individuals in the middle and high exposure group had an adjusted odds ratio (OR) of 1.30 and 1.40 for hypertension, had a 3.08 and 2.82 mm Hg higher systolic blood pressure (SBP) levels, respectively, with an inverted “U” shaped dose-response relationship. Compared with the reference group of BPS, individuals in the second and third tertile had an adjusted OR of 1.49 and 1.48 for hypertension, had a 2.61 and 3.89 mm Hg increased levels of SBP, respectively, with a monotonic curve. No significant associations of BPF exposure with hypertension risk or blood pressure levels were found. BPA and BPS exposure were suggested to be associated with increased hypertension risk and blood pressure levels, with different dose-response relationships. Our findings have important implications for public health but require confirmation in prospective studies.