Environmental Science and Ecotechnology最新文献

A reduction in building occupancy can lead to stagnant water in plumbing, and the potential consequences for water quality have gained increasing attention. To investigate this, a study was conducted during the COVID-19 pandemic, focusing on water quality in four institutional buildings. Two of these buildings were old (>58 years) and large (>19,000 m²), while the other two were new (>13 years) and small (<11,000 m²). The study revealed significant decreases in water usage in the small buildings, whereas usage remained unchanged in the large buildings. Initial analysis found that residual chlorine was rarely detectable in cold/drinking water samples. Furthermore, the pH, dissolved oxygen, total organic carbon, and total cell count levels in the first draw of cold water samples were similar across all buildings. However, the ranges of heavy metal concentrations in large buildings were greater than observed in small buildings. Copper (Cu), lead (Pb), and manganese (Mn) sporadically exceeded drinking water limits at cold water fixtures, with maximum concentrations of 2.7 mg Cu L⁻¹, 45.4 μg Pb L⁻¹, 1.9 mg Mn L⁻¹. Flushing the plumbing for 5 min resulted in detectable residual at fixtures in three buildings, but even after 125 min of flushing in largest and oldest building, no residual chlorine was detected at the fixture closest to the building's point of entry. During the pandemic, the building owner conducted fixture flushing, where one to a few fixtures were operated per visit in buildings with hundreds of fixtures and multiple floors. However, further research is needed to understand the fundamental processes that control faucet water quality from the service line to the faucet. In the absence of this knowledge, building owners should create and use as-built drawings to develop flushing plans and conduct periodic water testing.

Contaminants, such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), heavy metals, and per and polyfluoroalkyl substances (PFASs), primarily reach the Arctic through long-range atmospheric and oceanic transport. However, local sources within the Arctic also contribute to the levels observed in the environment, including legacy sources and new sources that arise from activities associated with increasing commercial and industrial development. The City of Iqaluit in Frobisher Bay, Nunavut (Canada), has seen rapid population growth and associated development during recent decades yet remains a site of interest for ocean protection, where Inuit continue to harvest country food. In the present study, seven dated marine sediment cores collected in Koojesse Inlet near Iqaluit, and from sites in inner and outer Frobisher Bay, respectively, were analyzed for total mercury (THg), major and trace elements, PAHs, PCBs, and PFASs. The sedimentary record in Koojesse Inlet shows a period of Aroclor 1260-like PCB input concurrent with military site presence in the 1950–60s, followed by decades of input of pyrogenic PAHs, averaging about ten times background levels. Near-surface sediments in Koojesse Inlet also show evidence of transient local-source inputs of THg and PFASs, and recycling or continued slow release of PCBs from legacy land-based sources. Differences in PFAS congener composition clearly distinguish the local sources from long-range transport. Outside Koojesse Inlet but still in inner Frobisher Bay, 9.2 km from Iqaluit, sediments showed evidence of both local source (PCB) and long-range transport. In outer Frobisher Bay, an up-core increase in THg and PFASs in sediments may be explained by ongoing inputs of these contaminants from long-range transport. The context for ocean protection and country food harvesting in this region of the Arctic clearly involves both local sources and long-range transport, with past human activities leaving a long legacy insofar as levels of persistent organic pollutants are concerned.

Reducing environmental pollution is a critical goal in global environmental economics and economic development. The European Union (EU) faces environmental challenges due to its development activities. Here we present a comprehensive approach to assess the impact of carbon dioxide (CO₂) emissions, energy consumption (EC), population structure (POP), economy (GDP), and policies on the environment within the EU using the environmental Kuznets curve (EKC). Our research reveals that between 1990 and 2019, the EU-27 experienced an increase of +1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p < 0.05), while CO₂ emissions decreased by 24.25 million tonnes (Mt) per year (p < 0.05). The highest reduction in CO₂ emissions occurred in Germany (−7.52 Mt CO₂ annually), and the lowest in Latvia (−0.087 Mt CO₂ annually). The empirical EKC analysis shows an inverted-U shaped relationship between GDP and CO₂ emissions in the EU-27. Specifically, a 1% increase in GDP results in a 0.705% increase in carbon emission, while a 1% increase in GDP² leads to a 0.062% reduction in environmental pollution in the long run (p < 0.01). These findings indicate that economic development within the EU has reached a stage where economic growth positively impacts the environment. Overall, this study provides insights into the effectiveness of environmental policies in mitigating degradation and promoting green growth in the EU 27 countries.

Air pollution threatens human health, necessitating effective and convenient air quality monitoring. Recently, there has been a growing interest in using camera images for air quality estimation. However, a major challenge has been nighttime detection due to the limited visibility of nighttime images. Here we present a hybrid deep learning model, capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images. Our model, which integrates a convolutional neural network (CNN) and long short-term memory (LSTM), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including PM_2.5 and PM₁₀ concentrations, as well as the air quality index (AQI). Compared to independent CNN networks that solely extract spatial features, our model demonstrates superior accuracy on self-constructed datasets with R² = 0.94 and RMSE = 5.11 μg m⁻³ for PM_2.5, R² = 0.92 and RMSE = 7.30 μg m⁻³ for PM₁₀, and R² = 0.94 and RMSE = 5.38 for AQI. Furthermore, our model excels in daytime air quality estimation and enhances nighttime predictions, elevating overall accuracy. Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model, reaffirming its applicability and superiority for air quality monitoring.

The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system management. The proliferation of model complexity results in more significant uncertainty and computational requirements. However, conventional model calibration methods are insufficient in dealing with extensive computational time and limited monitoring samples. Here we introduce a novel machine learning system designed to expedite parameter optimization with limited data and boost efficiency in parameter search. MLPS, termed the machine learning parallel system for fast parameter search of integrated process-based models, aims to enhance both the performance and efficiency of the integrated model by ensuring its comprehensiveness, accuracy, and stability. MLPS was constructed upon the concept of model surrogation + algorithm optimization using Ant Colony Optimization (ACO) coupled with Long Short-Term Memory (LSTM). The optimization results of the Integrated sewer network and urban river model demonstrate that the average relative percentage difference of the predicted river pollutant concentrations increases from 1.1 to 6.0, and the average absolute percent bias decreases from 124.3% to 8.8%. The model outputs closely align with the monitoring data, and parameter calibration time is reduced by 89.94%. MLPS enables the efficient optimization of integrated process-based models, facilitating the application of highly precise complex models in environmental management. The design of MLPS also presents valuable insights for optimizing complex models in other fields.

The commonly used Poisson rectangular pulse (PRP) model, employed for simulating high-resolution residential water consumption patterns (RWCPs), relies on calibration via medium-resolution RWCPs obtained from practical measurements. This introduces inevitable uncertainty stemming from the measured RWCPs, which consequently impacts the precision of model simulations. Here we enhance the accuracy of the PRP model by addressing the uncertainty of RWCPs. We established a critical sampling size of 2000 household water consumption patterns (HWCPs) with a data logging interval (DLI) of 15 min to attain dependable RWCPs. Through Genetic Algorithm calibration, the optimal values of the PRP model's parameters were determined: pulse frequency λ = 91 d⁻¹, mean of pulse intensity E(I) = 0.346 m³ h⁻¹, standard deviation of pulse intensity STD(I) = 0.292 m³ h⁻¹, mean of pulse duration E(D) = 40 s, and standard deviation of pulse duration STD(D) = 55 s. Furthermore, validation was conducted at both HWCP and RWCP levels. We recommend a sampling size of ≥2000 HWCPs and a DLI of ≤30 min for PRP model calibration to balance simulation precision and practical implementation. This study significantly advances the theoretical foundation and real-world application of the PRP model, enhancing its role in urban water supply system management.

Despite increasing alarms over the health impacts of microplastics (MPs) due to their detection in human organs and feces, precise exposure evaluations remain scarce. To comprehend their risks, there is a distinct need to prioritize quantitive estimates in MP exposome, particularly at the environmentally-realistic level. Here we used a method rooted in real-world MP measurements and activity patterns to determine the daily intake of MPs through inhalation and from ground dust/soil ingestion. We found that nearly 80% of this intake comes from residential sectors, with activity intensity and behavioral types significantly affecting the human MP burden. The data showed a peak in MP exposure for those aged 18–64. When compared to dietary MP intake sources like seafood, salt, and water, we identified a previously underestimated exposure from inhalation and dust/soil ingestion, emphasizing the need for more realistic evaluations that incorporate activity factors. This discovery raises questions about the accuracy of past studies and underscores MP's potential health risks. Moreover, our time-based simulations revealed increased MP intake during the COVID-19 lockdown due to more surface dust ingestion, shedding light on how global health crises may inadvertently elevate MP exposure risks.

The distribution and fractionation of persistent organic pollutants (POPs) in different matrices refer to how these pollutants are dispersed and separated within various environmental compartments. This is a significant study area as it helps us understand the transport efficiencies and long-range transport potentials of POPs to enter remote areas, particularly polar regions. This study provides a comprehensive review of the progress in understanding the distribution and fractionation of POPs. We focus on the contributions of four intermedia processes (dry and wet depositions for gaseous and particulate POPs) and determine their transfer between air and soil. These processes are controlled by their partitioning between gaseous and particulate phases in the atmosphere. The distribution patterns and fractionations can be categorized into primary and secondary types. Equations are developed to quantificationally study the primary and secondary distributions and fractionations of POPs. The analysis results suggest that the transfer of low molecular weight (LMW) POPs from air to soil is mainly through gas diffusion and particle deposition, whereas high molecular weight (HMW) POPs are mainly via particle deposition. HMW-POPs tend to be trapped near the source, whereas LMW-POPs are more prone to undergo long-range atmospheric transport. This crucial distinction elucidates the primary reason behind their temperature-independent primary fractionation. However, the secondary distribution and fractionation can only be observed along a temperature gradient, such as latitudinal or altitudinal transects. An animation is produced by a one-dimensional transport model to simulate conceptively the transport of CB-28 and CB-180, revealing the similarities and differences between the primary and secondary distributions and fractionations. We suggest that the decreasing temperature trend along latitudes is not the major reason for POPs to be fractionated into the polar ecosystems, but drives the longer-term accumulation of POPs in cold climates or polar cold trapping.

The retention of dense and well-functioning microbial biomass is crucial for effective pollutant removal in several biological wastewater treatment technologies. High solids retention is often achieved through aggregation of microbial communities into dense, spherical aggregates known as granules, which were initially discovered in the 1980s. These granules have since been widely applied in upflow anaerobic digesters for waste-to-energy conversions. Furthermore, granular biomass has been applied in aerobic wastewater treatment and anaerobic ammonium oxidation (anammox) technologies. The mechanisms underpinning the formation of methanogenic, aerobic, and anammox granules are the subject of ongoing research. Although each granule type has been extensively studied in isolation, there has been a lack of comparative studies among these granulation processes. It is likely that there are some unifying concepts that are shared by all three sludge types. Identifying these unifying concepts could allow a unified theory of granulation to be formed. Here, we review the granulation mechanisms of methanogenic, aerobic, and anammox granular sludge, highlighting several common concepts, such as the role of extracellular polymeric substances, cations, and operational parameters like upflow velocity and shear force. We have then identified some unique features of each granule type, such as different internal structures, microbial compositions, and quorum sensing systems. Finally, we propose that future research should prioritize aspects of microbial ecology, such as community assembly or interspecies interactions in individual granules during their formation and growth.

The uncontrolled discharge of industrial wastewater leads to a significant cadmium (Cd) accumulation in waste activated sludge (WAS), posing a serious threat to the steady operation of the anaerobic digestion (AD) system in wastewater treatment plants (WWTPs). Therefore, developing a viable approach to cope with the adverse effects of high-concentration Cd on the AD system is urgently required. This study aims to investigate the potential of using anionic polyacrylamide (APAM), a commonly used agent in WWTPs, to mitigate the adverse effects of Cd in a toxic amount (i.e., 5.0 mg per g total suspended solids (TSS)) on AD of WAS. The results showed that the effectiveness of higher APAM on Cd toxicity alleviation was less than that of lower APAM at the studied level (i.e., the effectiveness order was 1.5 mg APAM per g TSS > 3.0 mg APAM per g TSS > 6.0 mg APAM per g TSS). The moderate supplement of APAM (i.e., 1.5 mg per g TSS) recovered the accumulative methane yield from 190.5 ± 3.6 to 228.9 ± 4.1 mL per g volatile solids by promoting solubilization, hydrolysis, and acidification processes related to methane production. The application of APAM also increased the abundance of key microbes in the AD system, especially Methanolinea among methanogens and Caldilineaceae among hydrolyzers. Furthermore, APAM facilitated the key enzyme activities involved in AD processes and reduced reactive oxygen species (induced by Cd) production via adsorption/enmeshment of Cd by APAM. These findings demonstrate the feasibility of using moderate APAM to mitigate Cd toxicity during AD, providing a promising solution for controlling Cd or other heavy metal toxicity in WWTPs.