In this study, the performance of shortcut nitrification–denitrification (SCND) at different TC and SD stress conditions (0 μg/L, 1–97 days; 100 μg/L, 98–138 days; 500 μg/L, 139–175 days) was investigated. Higher level antibiotic stress (500 μg/L) led to the serious deterioration of nitrogen removal, and denitrification was more sensitive to antibiotic stress than nitrification. The dynamics of antibiotic resistance genes (ARGs) and microbial community were revealed by quantitative real-time PCR and 16S rDNA high-throughput sequencing, respectively. Tet-genes (tetA, tetQ, tetW), sul-genes (sulI, sulII), and mobile genetic element (intI1) in activated sludge increased by 1.2?~?2.5 logs with long-term exposure of antibiotic stress, and sulI, tetA, tetQ, and tetW were significantly positively correlated with intI1. Long-term antibiotics stress caused the decrease of most denitrifiers, and five genera were identified as the potential host of ARGs. The key impact factors of SCND drove the dynamics of ARGs and microbial community. Except for sulII gene, DO and FA were significantly positively correlated with ARGs, while FNA, NAR, and NO2?-N showed opposite effects to ARGs. Overall, maintaining relative lower DO, higher FNA, NAR, and NO2?-N conditions are not only benefit to the stable operation of SCND, but may also conducive to the control of ARG dissemination. This study provides theoretical basis on the control of ARGs in the SCND process.
Environmental Science and Pollution Research - The extensive application of chemically synthesized anionic surfactants would cause serious pollution of water and increase health risk to humans.... 相似文献
Local governments are the dominant players in haze pollution control; furthermore, financial power reconstruction affects the effectiveness of haze control. Government innovation preference achieves win-win results for environmental protection and economic development by increasing innovation support. Therefore, a moderating variable for government innovation preference was added to the fiscal decentralization effect on haze pollution, and their interactive effect on haze pollution was studied. This study was conducted in 30 provincial regions. Thus, the severity of regional haze pollution differs because of temporal heterogeneity and asynchronous development. Furthermore, we analyzed the impact on haze pollution from the perspectives of the temporal and spatial differences in different regions of China. The results indicate that (1) fiscal decentralization increases haze pollution, while government innovation preferences control it. (2) In a local evaluation model with a diversified background, fiscal decentralization restrains haze pollution, and pollution source complexity reduces government innovation preference’s control pollution function. The interaction term revealed that government innovation preferences had a significant moderating effect. (3) Fiscal decentralization and government innovation preferences control the heterogeneity of haze pollution in different regions.
Environmental Science and Pollution Research - Previous studies have reported that exposure to phthalates and polycyclic aromatic hydrocarbons (PAHs) is individually associated with altered semen... 相似文献
Zero-inflated data arise in many contexts. In this paper, we develop a zero-inflated Bayesian hierarchical model which deals with spatial effects, correlation among near-locating measurements as well as excess zeros simultaneously. Inference, including the sampling from the posterior distributions, predictions at new locations, and model selection, is carried out by using computationally efficient Markov chain Monte Carlo techniques. The posterior distributions are simulated using a Gibbs sampler with the embedded ratio-of-uniform method and the slice sampling algorithm. The approach is illustrated via an application to herbaceous data collected in the Missouri Ozark Forest Ecosystem Project. The results from the proposed model are compared with those generated from a non-zero inflated model. The proposed model fully incorporates the information from data collection and provides more reliable inference. A predictive $p$ value is computed for model checking and it indicates that the proposed model fits the data well. 相似文献
