The Universal Neighborhood Effect Averaging in Mobility-Dependent Environmental Exposures.

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Non-linear associations between noise level and people's short-term noise annoyance in different activity contexts.

Recent research has become increasingly interested in the on-linear associations between noise levels and people's short-term noise annoyance. However, there has been limited investigation into measuring short-term noise annoyance and how different activity contexts may affect these non-linear associations. To address this research gap, this study measured people's short-term noise annoyance using real-time Ecological Momentary Assessment (EMA) data and the Day Reconstruction Method's (DRM) recalled data. Corresponding noise levels were captured using Global Positioning Systems and portable noise sensors. Employing the Shapley additive explanations method, we examined the non-linear associations between noise level and people's real-time and recalled noise annoyance across different activity contexts. The results indicated that 1) People had greater sensitivity to noise levels in real-time annoyance (non-linear association threshold: 60 dB) compared to recalled annoyance, which had a higher non-linear association threshold of 70 dB. 2) The non-linear associations between noise level and people's real-time/recalled noise annoyance varied between different activity contexts. People tended to be more sensitive to noise in real-time annoyance than recalled annoyance on travel routes and at workplaces. 3) Among the factors examined, the contribution of noise level varied across activity contexts. Noise level contributed more significantly to people's real-time noise annoyance in outdoor recreational sites and on travel routes. These findings enhance our understanding of the non-linear association between noise level and people's short-term noise annoyance, moving beyond the linear paradigm. Policymakers should consider the non-linear relationships and different activity contexts when implementing noise control measures.

Confounding associations between green space and outdoor artificial light at night: Systematic investigations and implications for urban health.

Excessive urbanization leads to considerable nature deficiency and abundant artificial infrastructure in urban areas, which triggered intensive discussions on people's exposure to green space and outdoor artificial light at night (ALAN). Recent academic progress highlights that people's exposure to green space and outdoor ALAN may be confounders of each other but lacks systematic investigations. This study investigates the associations between people's exposure to green space and outdoor ALAN by adopting the three most used research paradigms: population-level residence-based, individual-level residence-based, and individual-level mobility-oriented paradigms. We employed the green space and outdoor ALAN data of 291 Tertiary Planning Units in Hong Kong for population-level analysis. We also used data from 940 participants in six representative communities for individual-level analyses. Hong Kong green space and outdoor ALAN were derived from high-resolution remote sensing data. The total exposures were derived using the spatiotemporally weighted approaches. Our results confirm that the negative associations between people's exposure to green space and outdoor ALAN are universal across different research paradigms, spatially non-stationary, and consistent among different socio-demographic groups. We also observed that mobility-oriented measures may lead to stronger negative associations than residence-based measures by mitigating the contextual errors of residence-based measures. Our results highlight the potential confounding associations between people's exposure to green space and outdoor ALAN, and we strongly recommend relevant studies to consider both of them in modeling people's health outcomes, especially for those health outcomes impacted by the co-exposure to them.

Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare.

As machine learning (ML) usage becomes more popular in the healthcare sector, there are also increasing concerns about potential biases and risks such as privacy. One countermeasure is to use federated learning (FL) to support collaborative learning without the need for patient data sharing across different organizations. However, the inherent heterogeneity of data distributions among participating FL parties poses challenges for exploring group fairness in FL. While personalization within FL can handle performance degradation caused by data heterogeneity, its influence on group fairness is not fully investigated. Therefore, the primary focus of this study is to rigorously assess the impact of personalized FL on group fairness in the healthcare domain, offering a comprehensive understanding of how personalized FL affects group fairness in clinical outcomes. We conduct an empirical analysis using two prominent real-world Electronic Health Records (EHR) datasets, namely eICU and MIMIC-IV. Our methodology involves a thorough comparison between personalized FL and two baselines: standalone training, where models are developed independently without FL collaboration, and standard FL, which aims to learn a global model via the FedAvg algorithm. We adopt Ditto as our personalized FL approach, which enables each client in FL to develop its own personalized model through multi-task learning. Our assessment is achieved through a series of evaluations, comparing the predictive performance (i.e., AUROC and AUPRC) and fairness gaps (i.e., EOPP, EOD, and DP) of these methods. Personalized FL demonstrates superior predictive accuracy and fairness over standalone training across both datasets. Nevertheless, in comparison with standard FL, personalized FL shows improved predictive accuracy but does not consistently offer better fairness outcomes. For instance, in the 24-h in-hospital mortality prediction task, personalized FL achieves an average EOD of 27.4% across racial groups in the eICU dataset and 47.8% in MIMIC-IV. In comparison, standard FL records a better EOD of 26.2% for eICU and 42.0% for MIMIC-IV, while standalone training yields significantly worse EOD of 69.4% and 54.7% on these datasets, respectively. Our analysis reveals that personalized FL has the potential to enhance fairness in comparison to standalone training, yet it does not consistently ensure fairness improvements compared to standard FL. Our findings also show that while personalization can improve fairness for more biased hospitals (i.e., hospitals having larger fairness gaps in standalone training), it can exacerbate fairness issues for less biased ones. These insights suggest that the integration of personalized FL with additional strategic designs could be key to simultaneously boosting prediction accuracy and reducing fairness disparities. The findings and opportunities outlined in this paper can inform the research agenda for future studies, to overcome the limitations and further advance health equity research.

Nonstationary relationships among individuals' concurrent exposures to noise, air pollution and greenspace: A mobility-based study using GPS and mobile sensing data.

Individuals are often exposed to multiple environmental factors simultaneously. Understanding their joint effects is essential for developing effective public health policies. However, there has been a lack of research examining individuals' concurrent exposures to multiple environmental factors during people's daily mobility. To address this gap, this study investigated the relationships between and geographic patterns of individual exposures to air pollution (PM2.5), noise and greenspace using individual-level real-time GPS and mobile sensing data collected in outdoor environments. The findings indicate that the relationships between individual exposures to air pollution, noise and greenspace vary across different value ranges of exposures. The study also reveals that people's concurrent exposures to multiple environmental factors exhibit spatial nonstationary and strong clustering patterns. These results highlight the importance of considering spatial nonstationary and spatial heterogeneity of environmental exposures in understanding the relationships between multiple exposures in environmental health research.

Synthesis of a carbon-wrapped microsphere MoO2/Mo2C heterojunction as an efficient electrocatalyst for the oxygen reduction reaction and the hydrogen evolution reaction.

The development of non-noble metal catalysts for the optimization of conversion and storage devices is an important research topic. Hence, the microsphere MoO2/Mo2C/C heterojunction composites, which play an important role in the oxygen reduction reaction (ORR) and the hydrogen evolution reaction (HER), were synthesized using the solvothermal-sintering method. The results revealed that the as-prepared composite exhibited better ORR and HER catalytic performances than those of MoO2/Mo2C and Vulcan XC-72R (carbon black), and approaching that of commercial Pt/C. At the same time, it has a superior methanol tolerance and electrochemical stability than that of the commercial Pt/C. The excellent performance may be attributed to the synergistic effect of the MoO2/Mo2C heterostructure, highly conductive Vulcan XC-72R, and oxygen vacancies (Ov). This research offers new insights into the design and synthesis of cost-effective, environmentally friendly heterojunction composite catalysts used as a high-performance cathode material in fuel cells and water splitting.

Perceiving noise in daily life: How real-time sound characteristics affect personal momentary noise annoyance in various activity microenvironments and times of day.

Annoyance is a major health burden induced by environmental noise. However, our understanding of the health impacts of noise is seriously undermined by the fixed contextual unit and limited sound characteristics (e.g., the sound level only) used in noise exposure assessments as well as the stationarity assumption made for exposure-response relationships. To address these limitations, we analyze the complex and dynamic relationships between personal momentary noise annoyance and real-time noise exposure in various activity microenvironments and times of day, taking into account individual mobility, multiple sound characteristics and nonstationary relationships. Using real-time mobile sensing, we collected individual data of momentary noise annoyance, real-time noise exposure as well as daily activities and travels in Hong Kong. A new sound characteristic, namely sound increment, is defined to capture the sudden increase in sound level over time and is used along with the sound level to achieve a multi-faceted assessment of personal real-time noise exposure at the moment of annoyance responses. Further, the complex noise exposure-annoyance relationships are learned using logistic regression and random forest models while controlling the effects of daily activity microenvironments, individual sociodemographic attributes and temporal contexts. The results indicate that the effects of the real-time sound level and sound increment on personal momentary noise annoyance are nonlinear, despite the overall significant and positive impacts, and different sound characteristics can produce a joint effect on annoyance. We also find that the daily activity microenvironments and individual sociodemographic attributes can affect noise annoyance and its relationship with different sound characteristics to varying degrees. Due to the temporal changes in daily activities and travels, the noise exposure-annoyance relationships can also vary over different times of the day. These findings can inform both local governments and residents with scientific evidence to promote the creation of acoustically comfortable living environments.

Mo2C promoted electrocatalysis of the Pt/Mo2C (C) heterostructure for a superior hydrogen evolution reaction.

Constructing a unique electrochemical interface to enhance the catalytic capacity of Pt-based catalysts is indispensable for wider application of the hydrogen evolution reaction (HER). Herein, platinum-analogous molybdenum carbide (Mo2C) was combined with a lower content of Pt to construct the Pt/Mo2C (C) heterostructure via a solid-phase method, using ammonium molybdate as the precursor. Vulcan-C served as a support to promote the distribution of the Pt and Mo2C heterostructure, and cooperative effects between Pt and the Mo2C heterostructure contributed to the significantly improved catalytic capacity of Pt. The obtained Pt/Mo2C (C) exhibits superior HER activity and enhanced long-term durability in the acidic medium, with a low overpotential of 38 mV at 10 mA cm-2 and a low Tafel slope of 24 mV dec-1. In particular, a drastically enhanced amount of H2 production can be achieved (6837.28 mmol h-1 g-1). This facile approach not only provides a new pathway for constructing novel heterostructures but also gives an insight into the design of cost-effective Pt-based materials for an efficient HER.

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research.

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.

Airborne infection risks of SARS-CoV-2 in U.S. schools and impacts of different intervention strategies.

The potential airborne transmission of SARS-CoV-2 has triggered concerns as schools continue to reopen and resume in-person instruction during the current COVID-19 pandemic. It is critical to understand the risks of airborne SARS-CoV-2 transmission under different epidemiological scenarios and operation strategies for schools to make informed decisions to mitigate infection risk. Through scenario-based analysis, this study estimates the airborne infection risk of SARS-CoV-2 in 111,485 U.S. public and private schools and evaluates the impacts of different intervention strategies, including increased ventilation, air filtration, and hybrid learning. Schools in more than 90% of counties exhibit infection risk of higher than 1%, indicating the significance of implementing intervention strategies. Among the considered strategies, air filtration is found to be most effective: the school average infection risk when applying MERV 13 is over 30% less than the risk levels correlating with the use of increased ventilation and hybrid learning strategies, respectively. For most schools, it is necessary to adopt combined intervention strategies to ensure the infection risk below 1%. The results provide insights into airborne infection risk in schools under various scenarios and may guide schools and policymakers in developing effective operations strategies to maintain environmental health.

Integrated environment-occupant-pathogen information modeling to assess and communicate room-level outbreak risks of infectious diseases.

Microbial pathogen transmission within built environments is a main public health concern. The pandemic of coronavirus disease 2019 (COVID-19) adds to the urgency of developing effective means to reduce pathogen transmission in mass-gathering public buildings such as schools, hospitals, and airports. To inform occupants and guide facility managers to prevent and respond to infectious disease outbreaks, this study proposed a framework to assess room-level outbreak risks in buildings by modeling built environment characteristics, occupancy information, and pathogen transmission. Building information modeling (BIM) is exploited to automatically retrieve building parameters and possible occupant interactions that are relevant to pathogen transmission. The extracted information is fed into an environment pathogen transmission model to derive the basic reproduction numbers for different pathogens, which serve as proxies of outbreak potentials in rooms. A web-based system is developed to provide timely information regarding outbreak risks to occupants and facility managers. The efficacy of the proposed method was demonstrated by a case study, in which building characteristics, occupancy schedules, pathogen parameters, as well as hygiene and cleaning practices are considered for outbreak risk assessment. This study contributes to the body of knowledge by computationally integrating building, occupant, and pathogen information modeling for infectious disease outbreak assessment, and communicating actionable information for built environment management.

Three 3D Co(ii) cluster-based MOFs constructed from polycarboxylate acids and bis(imidazole) ligands and their derivatives: magnetic properties and catalytic performance for the ORR.

Three 3D polymeric complexes containing polynuclear Co(ii) units, [Co3(bpda)3(bib)2]·H2O (1), [Co5(OH)2(H2O)2(bpda)4(bib)2]·2H2O (2), and [Co2(OH)(bib)(bpt)]·H2O (3) (H2bpda = biphenyl-2,4'-dicarboxylic acid, H3bpt = biphenyl-3,4',5-tricarboxylic acid, bib = 1,4-bis(1-imidazolyl)benzene), have been prepared by urothermal synthesis. Complex 1 exhibits an unusual (4,6)-connected 3D network based on binuclear Co(ii) clusters and Co2+ centers bridged by bpda2- and bib ligands. Complex 2, containing a novel pentanuclear Co(ii) cluster, features a porous 3D MOF with a regular nanosized tunnel. Complex 3 contains an interesting tetranuclear Co(ii) cluster and displays an amazing 3D porous MOF built from double-layered 2D [Co2(µ3-OH)(bpt)3]n networks pillared by two rows of bib spacers. In the title complexes, polycarboxylate acids serve to construct and stabilize the polynuclear Co(ii) clusters, while bis(imidazole)s help to expand the dimensionality of MOFs. Complexes 2 and 3 show antiferromagnetic properties. Compound 3 with high surface area, high yield, high purity, and high chemical stability was used as a precursor to fabricate Co,N-codoped porous carbon materials by simple carbonization under an Ar atmosphere. The catalytic properties of the Co,N-codoped porous carbon material derived from complex 3 (denoted as CoNC-3) as an ORR electrocatalyst were studied. CoNC-3 displays superior electrocatalytic performances for the ORR.

Fractionation and ecological risk of metals in urban river sediments in Zhongshan City, Pearl River Delta.

Surface sediments collected from nine urban rivers located in Zhongshan City, Pearl River Delta, were analyzed for total concentration of metals with digestion and chemical fractionation adopting the modified European Community Bureau of Reference (BCR) sequential extraction procedure. The results showed that concentration and fractionation of metals varied significantly among the rivers. The total concentration of eight metals in most rivers did not exceed the China Environmental Quality Standard for Soil, Grade III. The potential ecological risk of metals to rivers were related to the land use patterns, in the order of manufacturing areas > residential areas > agriculture areas. The concentration of Pb in the reducible fraction was relatively high (60.0-84.3%). The dominant proportions of Cd, Zn and Cu were primary in the non-residual fraction (67.0%, 71.8% and 81.4% on average respectively), while the percentages of the residual fractions of Cr and Ni varied over a wide range (43-85% and 24-71% respectively). The approaches of the Håkanson ecological risk index and Secondary Phase Enrichment Factor were applied for ecological risk assessment and metal enrichment calculation. The results indicated Hg and Cd had posed high potential ecological risk to urban rivers in this region. Meanwhile, there was widespread pollution and high enrichment of Cu in river sediments in this region. Multiple regression analysis showed that five water quality parameters (pH, DO, COD(Mn), NH(4)(+)-N, TP) had little influence on the distribution of metal fractionation. This result revealed that the ecological risk of metals was not eliminated along with the improvement in water quality. Correlation studies showed that among the metals, Group A (Cd, As, Pb, Zn Hg, r = 0.730-0.924) and Group B (Cr, Cu, Ni, r = 0.815-0.948) were obtained, and the metal contaminations were from industrial activities rather than residential.