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Organosulfates (OSs) are ubiquitous aerosol components, which has seen intense research over years. However, spatial and diurnal variations in OS formation in polluted atmospheres remain poorly understood. In this study, 130 OS species were quantified (or semi-quantified) in ambient fine particulate matter (PM 2.5) collected in urban and suburban Shanghai (East China) in the summer of 2021. Isoprene- and monoterpene-derived OSs were dominant OS groups (averaging 51 % and 19 % of total quantified OSs, respectively), likely indicating a large biogenic contribution to OS formation in summer. Most OSs peaked during daytime, while monoterpene-derived nitrooxy-OSs (NOS m) increased during nighttime. Accordingly, OSs were largely produced via daytime formation processes, rather than nighttime chemistry, except for NOS m. Additionally, although OS formation in the urban and suburban areas exhibited similar diurnal variations, the average concentrations of biogenic and anthropogenic OSs decreased significantly from the urban site to the suburban site. Furthermore, we concretized daytime OS formation based on the interactions among OSs, ultraviolet (UV), ozone (O 3), and sulfate (SO 42-). Indeed, the concentrations of most OSs were significantly correlated with the values of UV[O 3 ][SO 42- ] during daytime in both urban and suburban Shanghai. In particular, the correlation between major OSs and UV[O 3 ][SO 42- ] was stronger than the correlation of major OSs with O 3 and SO 42- ; moreover, there was no significant correlation between major OSs and UV. Thus, higher urban OS events were attributed to the enhanced photochemical processes and sulfate level in the urban area. Overall, this study provides field evidence for the influence of photochemical processes and anthropogenic sulfate on OS formation and has important implications for the mitigation of organic particulate pollution. [ABSTRACT FROM AUTHOR]

Evidence on the relationship between prenatal exposure to NO2 and CO and spontaneous abortion (SAB) is insufficient. We investigated whether there is an association between maternal exposure to nitrogen dioxide (NO2) and carbon monoxide (CO) before and during pregnancy and SAB. We conducted a case-control study using medical records of 2445 pregnant women who admitted for abortion prior to 20 weeks of gestational age from January 2014 to December 2019 at a tertiary-care hospital in Shanghai, China. Of the 2445 participants, 1075 were SAB cases and 1370 were healthy controls (underwent elective abortions). Maternal exposure to NO2 and CO before and during pregnancy was estimated using daily air pollution concentration data. Multivariable logistic regression models were constructed to quantify the relationships between maternal exposure to NO2 or CO and the risk of SAB while controlling for potential confounders. NO2 exposure levels during pregnancy were significantly higher in SAB cases than in healthy controls (42.26 vs. 40.67, p <.01). NO2 exposure during pregnancy was positively associated with the risk of SAB. An interquartile range (16 μg/m3) increase in NO2 exposure was associated with 68% increase in the odds of SAB (OR = 1.68, 95% CI, 1.28, 2.21). Analyses of associations by quartile of NO2 exposure showed that elevated NO2 exposure during pregnancy was associated with increased odds of SAB in linear dose–response manners. Compared with the lowest quartile of NO2 exposure, the odds of SAB in the fourth quartile of NO2 exposure increased 61% (OR = 1.61, 95% CI, 1.03–2.53). No associations of CO exposure with SAB risk were observed. Our study suggested that exposure to NO2 during early pregnancy was associated with increased risk of SAB. Further studies are needed to confirm our results and explore the potential biological mechanism underlying these associations. [ABSTRACT FROM AUTHOR]

The scale of fluctuation (SOF) is the critical parameter to describe the soil spatial variability, which significantly influences the embedded geostructures. Due to the limited data in the horizontal direction, horizontal SOF estimation is relatively challenging and not well studied yet. This paper aims to develop an efficient convolutional neural network (CNN)-based approach for estimating the horizontal SOF by coupling random field and limited CPT data. Two or three columns (i.e. pseudo-CPT) were selected from the simulated 2D random field with prescribed SOF at the same spacing and combined into a two-dimensional matrix as input data to train the CNN model, namely CNN2 and CNN3 models. The dataset of CNN2 and CNN3 models contains 196,670 and 149,420 samples. Results on the training and testing datasets show that the trained CNN model has a good estimation performance as the mean squared error value is less than 0.1 and the correlation coefficient value is larger than 0.99. The effectiveness of trained CNN models was further verified by the new simulated CPT data with untrained SOF from the random field and CPT data from real site in Hollywood, South Carolina. The excellent agreement indicates that the trained CNN model has the ability to capture the horizontal SOF for limited CPT data from the actual project. Finally, the collected CPT data from the Shanghai site was applied for application. The COV of the estimated results of CNN3 and CNN2 models for the Shanghai site is 0.09 and 0.40, indicating the estimation performance of the CNN3 model has less variability than the CNN2 model. The proposed method provides the potential to characterize the soil spatial variability using very limited CPT data. [ABSTRACT FROM AUTHOR]

Traffic restriction policy (TRP) is common practice to alleviate urban congestion and air pollution. However, effects of TRP for non-local vehicles and elevated expressways on local and regional air quality are understudied. Using air pollutant data collected by ground-based national and roadside environmental monitoring stations and field measurements, comparative research design and regression discontinuity analysis were performed to investigate the local and regional variations in air pollutant concentrations with and without the TRP. Results indicate that traffic restriction significantly decreased the local and regional concentration levels of primary gaseous pollutants (e.g., NOX and CO), particularly under more stringent TRP during China International Import Expo, which also brought an ozone increase due to the reduced effect of NOX titration. However, the TRP only exhibited limited impacts on mitigating regional particulate pollution. These findings suggest that the air quality improvements due to the non-local TRP highly depend on the types of pollutants and the spatial locations. [Display omitted] • Pollutant data from Shanghai monitoring networks are used for policy evaluation. • Comparative research design and regression discontinuity analysis are performed. • Restriction brings a sharper decline in primary gaseous pollutants than particles. • Restriction hardly improves urban air quality of Shanghai where particles dominate. • Air quality impacts highly depend on the types of pollutants and spatial locations. [ABSTRACT FROM AUTHOR]

The geo-structures embedded in the multiple variable strata could be significantly affected by the geological uncertainty. The quantitative evaluation of geological uncertainty and its influence on the structural safety of embedded tunnels are seldom studied in the past. This paper aims to analyse the effect of geological uncertainty on the structural performance of tunnel using the proposed stochastic geological modelling framework. The geological uncertainty is characterized using an improved coupled Markov chain model based on sparse limited boreholes. A mapping approach is presented to solve the mesh asymmetry problem between the simulated strata and the numerical tunnel model. The tunnel structural performance analysis is then conducted based on the combined model considering the geological uncertainty and tunnel structure. A geological uncertainty index (GUI) is proposed to quantitatively evaluate the level of uncertainty of each borehole and the whole site. The effect of the borehole layout scheme on uncertainty evaluation of factor of safety of tunnel structure is investigated by a large number of stratigraphic realizations. Boreholes collected from Norway with relatively more considerable variability and from Shanghai with relatively more minor variability are adopted as case studies to illustrate the proposed probabilistic analysis framework. The results show that the boreholes with larger GUI values and closer to tunnel locations have a greater weight to affect the embedded tunnel structural performance in uncertain geological strata. [ABSTRACT FROM AUTHOR]

• 1.A well-documented settlement recovery case of shield tunnel is introduced. • 2.Grouting effect on deformation in spatial and temporal dimensions is revealed. • 3.Volume-based grouting efficiency is used to study the deformation characteristics. • 4.Grouting efficiency is low, horizontal convergence increases during the grouting. This paper presents a case study introducing the effect of soil grouting conducted to repair the longitudinal performance of shield tunnel in Shanghai. The maximum of cumulative settlement of the shield tunnel was approximately 15 mm with an averaged increasing rate as high as 0.50 mm/d caused by a new tunneling project underneath. The grouting reinforcement for soils under the invert of tunnel was thus carried out in the distorted section. A full process of deformational responses in terms of settlement and horizontal convergence to the grouting was measured to analyze the influence of grouting on performance recovery in the longitudinal direction. Volume-based grouting efficiency was defined for evaluating the contribution of grouting to structural deformation recovery. The results showed that the settlement is reduced linearly with the increasing of grouting volume. However, horizontal convergence increased during the grouting process and the maximum convergence increment was about 63 % of the magnitude of settlement reduction. Meanwhile, grouting efficiency was found to be relatively low. An average of 9.03 % of the grout volume is contributed to the recovery of settlement trough, while 2.75 % acted on the increase of convergence. Besides, continuous grouting can effectively recover tunnel settlement and remain a safe state. [ABSTRACT FROM AUTHOR]

Objective To study the associations of pedestrian injuries with age, income and educational level in Shanghai and to analyze the relative disease burden. Methods Information on pedestrian-related cases and deaths were collected from 494 hospitals and mortality registry systems from 1992 to 2010, and a multistage cluster sampling survey conducted in 2006. Logistic regression model was used in the analyses. Results The age group of 5-9 had the highest mortality and morbidity among children. Mortality increased obviously among those aged 60 or above. Individuals with an educational level under the primary school and with the lower family average income were more likely to suffer pedestrian-related injuries. Multivariate Logistic analysis demonstrated that lower income and lower educational level increased the risk of pedestrian injuries with the odds ratio of 1.40 (95% CI: 1.15-1.71) and 1.70 (95% CI: 1.20-2-40), respectively. About 13.54% of the share of GDP for the healthcare, social security and welfare industries in Shanghai was occupied by the burden of pedestrian-related injuries in 2006. Conclusion Pedestrian-related injury has inverse association with victims' income and educational level. Children of 5-9 years old and adults over 60 with lower educational level and lower monthly income are the target persons to be intervened. [ABSTRACT FROM AUTHOR]

Objective To study age and educational level and their relationship with fall-related injuries in Shanghai and to analyze the relevant costs. Methods Multistage cluster sampling was used for the selection of participants and standardized questionnaires were used for the information collection in 2006. Information on cases and deaths caused by fall-related injuries were obtained from 494 hospitals as well as from the mortality registry systems from 2001 till 2010. Results Of 45 857 participates, 674 suffered from fall-related injuries with the largest proportion among all injuries. The fall-related mortality increased from 10.63 per 100 000 in 2001 to 14.11 per 100 000 in 2010. The under-five mortality rate was the highest among children aged 0-14 years. Mortality increased dramatically among those aged 55 or above for the female and aged 60 or older for the male. Individuals with an educational level under the primary school were more likely to suffer fall-related injuries, accounting for 72.66% of all deaths and 49.24% of nonfatal cases respectively. The annual burden of fall-related injuries equated to 25.90% of the share of GDP for the healthcare, social security and welfare industries in 2006. Conclusion Fall-related injuries were inversely related to victims' educational level. Children under the age of 5, women over 55 years old and men over 60 years old with an educational level lower than the primary school are the most risky groups of populations for intervention measures. [ABSTRACT FROM AUTHOR]

Summary: Our hospital (Shanghai Skin Diseases & STD Hospital) started to study 5-aminolevulinic acid-mediated photodynamic therapy (ALA-PDT) in 1996. So far, we have treated 76 cases of skin cancer and pre-cancer using topical ALA-PDT. They included squamous cell carcinoma (SCC), basal cell carcinoma (BCC), Bowen''s disease (BD), mammary and extramammary Paget disease, actinic keratosis (AK) and erythroplasia of Queyrat. In this overview article, we would like to present several representative cases and discuss our experience. [Copyright &y& Elsevier]

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%–48.8% drop in the concentration of NO 2 has been observed in the four metropolises compared with the same period in 2018–2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO 2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m3) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality. • The interruption and recovery of air pollution during the COVID-19 was evaluated. • Regression Discontinuity Design (RDD) has been used for statistical analysis. • Data of four major Chinese cites were utilized for characterizing and modelling. • The year-on-year NO 2 and PM 2.5 showed 25.3%–48.8% and 23.1%–46.2% drop separately. [ABSTRACT FROM AUTHOR]

Toll stations are traffic-related pollution hotspots, but the contribution of various factors to pollutant distributions has not been clearly recognized. This study conducted a field investigation of the spatiotemporal variations of pollutants on different lanes at a toll station in Shanghai, China. Quantitative analysis showed that the concentrations of black carbon (BC) and fine particles (PM 2.5) on the manual toll collection (MTC) lane were higher than those on the electronic toll collection (ETC) lane, and higher concentrations were identified on weekdays. Furthermore, the study introduced interaction terms between lane type and traffic conditions while constructing generalized additive models (GAMs) to reveal the internal relationship between concentrations and explanatory variables. The results of GAM analysis showed that the coupling effect of traffic volume and lane type was the most important force driving variations in BC, while background concentration was the dominant factor of PM 2.5 variations. The Extreme Gradient Boosting (XGBoost) models were developed to separately evaluate the influence of lane type and traffic volume on BC and PM 2.5 , indicating that lane type contributed 29.4% and 36.5% of BC and PM 2.5 variations respectively. Additionally, simulation demonstrated that in contrast to MTC, ETC can reduce BC pollution by 25.2% under the same traffic conditions. Finally, the respiratory deposition dose was used to compare the negative consequences of exposure between the ETC and MTC lane. This study revealed the general variation trends of pollutants and corresponding influencing factors at a toll station, and could provide implications for mitigating traffic-related pollution. • BC and PM 2.5 at a toll station are analyzed via field study and machine learning. • BC and PM 2.5 concentrations are verified to be higher on MTC compared to ETC. • GAM and XGBoost are used to reveal the impacts of lane, traffic and meteorology. • Lane type and BGC contribute the largest BC and PM 2.5 variations, respectively. • BC pollution is reduced by 25.2% on ETC through XGBoost simulation. [ABSTRACT FROM AUTHOR]

Traffic is a significant pollution source in cities and has caused various health and environmental concerns worldwide. Therefore, the improved understanding of traffic impacts on particle concentrations could help mitigate air pollution. In this study, field measurements were conducted beside an arterial road in Shanghai, China, to obtain the fine-scale temporal variations in traffic volumes, meteorological parameters, and size-resolved particle concentrations. The generalized additive model was developed to reveal their associations under different analysis intervals (time scales). Results indicate that on-road traffic greatly increased the concentration and variability of roadside particles. Variations of roadside fine particles (<1 μm, having high homogeneity) closely followed those at the background point, but background variations of coarse particles (having high heterogeneity) were negligible compared with the roadside point. Roadside particles showed high variability on the 10-s scale and presented frequent stochastic spikes above the background level. The particle variability was greatly reduced on the 60-min scale, exhibiting stable variations. The association between traffic and roadside particle variations was weak on a fine temporal scale (5-min scale). However, the association showed a pronounced increase when the analysis interval was increased (60-min scale). This is because extending the analysis interval could reduce the influence of random factors (sample randomness) and emission lagging effects on the results. Moreover, with the increase of the analysis interval, the improvement of associations between variables can be well characterized by exponential curves, which could help determine the optimal analysis interval. Additionally, heavy-duty vehicles showed a lot more significant influence on particle variations compared with that of light-duty vehicles, and should be regarded as the key governance target for on-road pollution control. These findings broaden the knowledge of traffic impacts on particle concentrations, which could provide implications in air pollution abatement. [Display omitted] • Impacts of traffic on particle variations were studied via field experiments. • The generalized additive model was used to assess the associations. • Traffic greatly increased the concentration and variability of roadside particles. • Traffic-particle associations improved with the increase of the analysis interval. • The improvement in associations can be well characterized by exponential curves. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has significantly inhibited global economic growth and impacted the environment. Some evidence suggests that lockdown strategies have significantly reduced traffic-related air pollution (TRAP) in regions across the world. However, the impact of COVID-19 on TRAP on roadside is still not clearly understood. In this study, we assessed the influence of the COVID-19 lockdown on the levels of traffic-related air pollutants in Shanghai. The pollution data from two types of monitoring stations—roadside stations and non-roadside stations were compared and evaluated. The results show that NO 2 , PM 2.5 , PM 10 , and SO 2 had reduced by ~30–40% at each station during the COVID-19 pandemic in contrast to 2018–2019. CO showed a moderate decline of 28.8% at roadside stations and 16.4% at non-roadside stations. In contrast, O 3 concentrations increased by 30.2% at roadside stations and 5.7% at non-roadside stations. This result could be resulted from the declined NOx emissions from vehicles, which lowered O 3 titration. Full lockdown measures resulted in the highest reduction of primary pollutants by 34–48% in roadside stations and 18–50% in non-roadside stations. The increase in O 3 levels was also the most significant during full lockdown by 64% in roadside stations and 33% in non-roadside stations due to the largest decrease in NO 2 precursors, which promote O 3 formation. Additionally, Spearman's rank correlation coefficients between NO 2 and other pollutants significantly decreased, while the values between NO 2 and O 3 increased at roadside stations. [Display omitted] • NO 2 , PM 2.5 , PM 10 and SO 2 reduced by ~30–40% in shanghai during the COVID-19 pandemic in contrast to 2018–2019. • CO showed a decline of 28.8% on roadside stations and 16.4% at non-roadside stations. • Decline of NOx from vehicles leads to an increase of O 3 on roadside (30.2%) and non-roadside (5.7%). • Full lockdown measures resulted in the highest reduction of primary pollutants. [ABSTRACT FROM AUTHOR]

Elevated expressways have been built in many cities worldwide to improve the accessibility and capability of urban road network. Meanwhile, intensive emissions from ground- and elevated-road traffic would significantly elevate personal exposure levels to air pollutants, such as fine particles (PM 1), carbon monoxide (CO) and black carbon (BC). However, their transport and dispersion mechanisms in urban residential areas have not been clearly recognized. In this study, we characterized temporal and vertical distribution patterns of traffic-emitted pollutants near an elevated expressway in near-road buildings. Vertically-resolved measurements of traffic emissions were made at different floors of a roadside building alongside the Inner Ring Viaduct in downtown Shanghai, China from January to June 2019. Results show that higher pollutant concentrations were observed during the morning (Local Time, UTC+8, LT 08–09) in winter and during the late afternoon (LT 17–18) in summer, the result of seasonal variations in vehicular activities and meteorological factors. Additionally, the vertical profiles of traffic-emitted pollutants all exhibited bimodal distribution patterns, which was more obvious for PM 1 than for CO and BC. The upper peak appeared slightly above noise barriers, attributable to the extra vehicular emissions on the elevated expressway. The lower peak occurred at ground level mainly due to on-road congested traffic flow, and the presence of the elevated road, which inhibited the dilution of ground-level vehicular exhausts. These findings reveal the general trends of the dispersion patterns of traffic emissions surrounding urban elevated expressways, and could provide implications in making control measures to mitigate air pollution exposure. Image 1 • Spatiotemporal distributions were analyzed for traffic emissions via field study. • The hourly variations of traffic-emitted pollutants changed greatly with season. • The vertical profiles of traffic-emitted pollutants exhibited bimodal patterns. • Two peaks appeared at ground level and slightly above noise barriers, respectively. • Traffic-emitted pollutants highly correlated to relative humidity and traffic flow. [ABSTRACT FROM AUTHOR]

The inherent spatial variability of soil properties is the main sauces of uncertainties in the site investigation, and it is commonly characterized using random field theory. In the context of random fields, the scale of fluctuation (SOF) is a significant parameter to reflect the spatial correlation between the two points of soil properties. However, it is challenging to estimate the SOF value accurately, especially when there are only limited project-specific test results, such as cone penetration test (CPT) data. This study aims to develop a convolutional neural network (CNN) approach to estimate the vertical SOF based on limited CPT data. The CNN model was constructed and trained by the simulated 15,000 CPT samples using random fields. The results show that the CNN model has excellent performance for estimating vertical SOF. The approach is validated and illustrated through newly simulated CPT data, eight real CPT data obtained from the literature, and three CPT data collected from the Shanghai site. The proposed scale factor method can solve the mismatch between the actual CPT depth and the required depth for input data of the CNN model, making the CNN model more widely applicable. • A CNN approach is developed to estimate vertical SOF for soil properties. • The approach is validated and illustrated using both real and simulated data. • The proposed scale factor method can make CNN model more widely applicable. • Promising estimation performance is achieved. [ABSTRACT FROM AUTHOR]