Environmental Research最新文献

In light of the growing need to mitigate climate change impacts, this study presents an innovative methodology combining ensemble machine learning with experimental data to accurately predict the carbon dioxide footprint (CO₂-FP) of fly ash geopolymer concrete. The approach employs adaptive boosting to enhance decision tree regression (DTR) and support vector regression (SVR), resulting in a robust predictive framework. The models used key material features, including fly ash concentration, fine and coarse aggregates, superplasticizer, curing temperature, and alkali activator levels. These features were tested across three configurations (Combo-1, Combo-2, Combo-3) to determine optimal predictor combinations, with Combo-3 consistently yielding the highest predictive accuracy. The performance of the developed models was assessed based on standard metric indicators like mean absolute error (MAE), root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), and correlation coefficient between the predicted and actual CO₂-FP. Results demonstrated that the Adaboost-DTR model with Combo-3 configuration achieved the best performance metrics during testing (CC = 0.9665; NSE = 0.9343), outperforming both standalone and other ensemble models. The findings underscore the value of feature selection and boosting techniques in accurately estimating CO₂ emissions for sustainable construction applications. This research offers remarkable benefits for policymakers and industry stakeholders aiming to optimize concrete compositions for environmental sustainability. The results support future integration with IoT systems to enable real-time CO₂ monitoring in construction materials. Finally, this study establishes a foundation for developing efficient CO₂-FP emission management tools.

Recent evidence suggests that exposure to per- and polyfluoroalkyl substances (PFAS) may increase the risk of different cancer types, such as kidney and testicular cancers. Instead, evidence for lung, head and neck, and thyroid cancer is sparse. Hence, we aimed to summarize available literature on the topic. We searched Pubmed and Scopus in January 2024 to retrieve relevant studies and estimated pooled relative risks (RRs) and 95% confidence intervals (CIs) for lung, head and neck, and thyroid cancers according to PFAS exposure using restricted maximum likelihood method. Pooled RRs for occupational or environmental PFAS exposure were 1.20 (95% CI: 1.12-1.28; I² = 0.0%, p_het = 0.9; n. studies = 9), 1.15 (95% CI: 0.96-1.37; I² = 0.0%, p_het = 0.7; n. studies = 3), and 1.54 (95% CI: 0.86-2.78; I² = 69.0%, p_het = 0.02; n. studies = 4) for lung, head and neck, and thyroid cancer, respectively. We did not find compelling evidence of publication bias for lung cancer (p = 0.3). Studies on statistically modelled serum PFAS levels did not support associations with these cancers. We found no positive associations between measured serum levels of 6 different types of PFAS and thyroid cancer. However, the pooled RR of two case-control studies nested within cohorts on the association between natural log-unit increase of perfluorooctanesulfonic acid (PFOS) and thyroid cancer was 1.51 (95% CI: 1.11-2.05; I² = 21.1%, p_het = 0.3). PFAS exposure may be associated with lung and thyroid cancer. Due to the limited number of studies and their limitations, further prospective studies with appropriate account of co-exposure with other carcinogens and detailed exposure assessment are needed to establish causality of observed associations.

Tidal-flow constructed wetlands (TFCWs) provide distinct advantages for nitrogen removal by enhancing microbial activity through dynamic water level fluctuations. However, effects of temperature on nitrogen transformation processes and microbial community dynamics in TFCWs remain unclear. We analyzed the effects of TFCWs on nitrogen transformation and microbial community structure under different temperature conditions (23, 16, 12, and 8 °C) through 140 days of temperature-controlled experiments. The nitrogen removal efficiency was considerably enhanced at 23 °C, with transformation rates for ammonia nitrogen (NH₄⁺-N) and total nitrogen (TN) reaching 9.28 ± 0.06 g/m³/day and 8.35 ± 0.08 g/m³/day, respectively. Conversely, at 8 °C, the nitrogen removal efficiency declined, with NH₄⁺-N and TN transformation rates decreasing to 7.38 ± 0.05 g/m³/day and 6.78 ± 0.05 g/m³/day, respectively. Temperature markedly influenced the microbial diversity and community structure, as evidenced by the considerably higher Shannon diversity indices for bacterial communities at 23 °C (5.12 ± 0.21) compared with those at 8 °C (4.52 ± 0.40). Positive microbial interactions were more prevalent at lower temperatures (12 and 8 °C), leading to stronger symbiotic relationships, although the network complexity diminished. The microbial community composition of taxa such as Firmicutes, Proteobacteria, and Thaumarchaeota exhibited greater resilience at lower temperatures. Changes in dissolved oxygen levels also drove changes in bacterial and archaeal communities. These findings underscore the pivotal role of temperature in regulating ecological function and nitrogen removal efficiency of TFCWs and highlight the importance of accounting for temperature variations in the design and management of wastewater treatment systems.

Granular size induces the operation performance variation of aerobic granular sludge reactor, but the profound reasons are unrevealed. This study investigated the influence of granular size distribution on the reactor operation under salt stress. The effective nitrogen removal was achieved at ≤4% salinity, but declined at 6% salinity. The phenomenon was determined by the granular size fraction. The small granules (d = 200-600 μm) fraction was 77%-81% at ≤4% salinity, while only 57.32% at 6% salinity. That was positively correlated with nitrite reductase (NIR) activity significantly (p < 0.01). Moreover, small granules exhibited smooth surface at ≤4% salinity. The efficient mass transfer area of granules was enlarged by the smooth surface, accelerating substrates mass transfer. Consequently, ammonia monooxygenase (AMO) activity was enhanced significantly (p < 0.05). Ammonia-oxidation bacteria, Nitrosomonas and nitrite-reduction bacteria, Paracoccus were dominated in small granules at 4% salinity, while loss at 6% salinity. Overall, small granules with smooth surface favored the enrichment of nitrogen removal microbes via substrate transfer enhancement, and improved the activity of AMO and NIR. Thus, the favorable nitrogen removal performance of aerobic granular sludge reactor was achieved at ≤4% salinity.

Co-landfill of municipal solid waste (MSW) and bottom ash (BA) has accelerated the scaling of the leachate collection systems (LCS). The matrix of biofilm formation and mineral deposition makes the scaling process in LCS more complicated. However, the fate of metals released from BA and the role of microorganisms in the leachate, which determine the chemical and biological scaling, are not well understood; the scale adsorption ability is little discussed. We analyzed the microorganism response and scale properties under various simulated landfill conditions with different MSW to BA ratios. The adsorption ability of the scales was evaluated through ultrasonic treatment. Scale characterization revealed that Ca²⁺ plays different roles with co-landfilled BA. Under BA-only landfilling conditions, Ca²⁺ was precipitated as CaCO₃, with a strong adsorption ability. The co-landfilling of BA and MSW resulted in the formation of a thicker scale compared to BA landfilling alone. Interestingly, the hydrophilic surface of the biofilm enhanced the descaling efficiency, achieving up to 85%. Microbial composition analysis at the genus level revealed that the co-landfilling with BA significantly influenced the microbial community. Particularly, BA enhanced the biofilm formation ability of the microorganisms. Additionally, the scales adhering to polyvinyl chloride (PVC) pipes developed a distinct microenvironment different from the leachate, with a noticeable increase in anaerobic bacteria. These findings offer new insights into scale control and pipeline failure caused by aging and corrosion.

The extensive expansion of impervious surfaces encroaches on green spaces and causes frequent urban waterlogging disasters. Previous studies have focused mainly on the influence of green space landscape pattern on waterlogging, with less attention given to green space morphological spatial pattern (MSPA). MSPA can be used to differentiate various types of land use morphologies from a microscopic perspective and reveal visualized spatial characteristics. Therefore, this study selected Shenzhen, a city with serious waterlogging problems, as the study area. The anthropogenic/natural environments and green space morphological spatial pattern were considered. Pearson correlation analysis and random forest regression were combined to investigate the influence of these drivers on the density of waterlogging hotspots and quantify the degree of importance for each driver. The results were supplemented with explanations using SHapley Additive exPlanations and Partial Dependence Plots. Pearson correlation analysis revealed that green space morphological spatial pattern, the proportion of green spaces, and the proportion of impervious surfaces were the dominant drivers. Additionally, the random forest regression showed that incorporating green space morphological spatial pattern and average tree height as potential drivers could strengthen the model's goodness-of-fit. While the proportion of impervious surfaces, the proportion of green spaces, and population density were important drivers, the green space morphological spatial pattern, specifically the "loop", "edge", and "core", was even more crucial and had an optimal design range. Therefore, green space morphological spatial pattern should be emphasized during the planning of "sponge cities" to maximize the ability of green spaces to mitigate waterlogging. In summary, our findings are expected to provide feasible suggestions for waterlogging control and green space planning.